@prefix vivo: . @prefix edm: . @prefix ns0: . @prefix dcterms: . @prefix skos: . vivo:departmentOrSchool "Science, Faculty of"@en, "Statistics, Department of"@en ; edm:dataProvider "DSpace"@en ; ns0:degreeCampus "UBCV"@en ; dcterms:creator "Ladak, Al-Karim Madatally"@en ; dcterms:issued "2010-10-15T11:33:36Z"@en, "1990"@en ; vivo:relatedDegree "Master of Science - MSc"@en ; ns0:degreeGrantor "University of British Columbia"@en ; dcterms:description "Weigh scaling is a method of estimating the total volume of timber harvested from a given region. The implementation of statistical sampling techniques in weigh scaling is described, along with related issues. A review of ratio estimators, along with variance estimators of the classical ratio estimator is conducted. The estimation of the variance of the estimated total volume is considered using jackknife- and bootstrap-based variance estimators. Weighted versions of the jackknife and bootstrap variance estimators are derived using influence functions and Fisher Information matrices. Empirical studies of analytic and resampling-based variance estimators are conducted, with particular emphasis on small sample properties and on robustness with respect to both the homoscedastic variance and zero-intercept population characteristics. With a squared error loss function, the resampling-based variance estimators are shown to perform very well at all sample sizes in finite populations with normally distributed errors. These estimators are found to have small negative biases for small sample sizes and to be robust with respect to heteroscedasticity."@en ; edm:aggregatedCHO "https://circle.library.ubc.ca/rest/handle/2429/29195?expand=metadata"@en ; skos:note "RESAMPLING-BASED VARIANCE ESTIMATORS IN RATIO ESTIMATION WITH APPLICATION TO WEIGH SCALING By Al-Karim Madatally Ladak B.Sc, The University of British Columbia, 1988 A THESIS SUBMITTED IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE FACULTY OF GRADUATE STUDIES THE DEPARTMENT OF STATISTICS We accept this thesis as conforming to the required standard THE UNIVERSITY OF BRITISH COLUMBIA September 1990 © Al-Karim Madatally Ladak, 1990 In presenting this thesis in partial fulfilment of the requirements for an advanced degree at the University of British Columbia, 1 agree that the Library shall make it freely available for reference and study. I further agree that permission for extensive copying of this thesis for scholarly purposes may be granted by the head of my department or by his or her representatives. It is understood that copying or publication of this thesis for financial gain shall not be allowed without my written permission. Department The University of British Columbia Vancouver, Canada DE-6 (2/88) Abstract Weigh scaling is a method of estimating the total volume of timber harvested from a given region. The implementation of statistical sampling techniques in weigh scaling is described, along with related issues. A review of ratio estimators, along with variance estimators of the classical ratio estimator is conducted. The estimation of the variance of the estimated total volume is considered using jackknife- and bootstrap-based variance estimators. Weighted versions of the jackknife and bootstrap variance estimators are derived using influence functions and Fisher Information matrices. Empirical studies of analytic and resampling-based variance estimators are conducted, with particular emphasis on small sample properties and on robustness with respect to both the homoscedastic variance and zero-intercept population characteristics. With a squared error loss function, the resampling-based variance estimators are shown to perform very well at all sample sizes in finite populations with normally distributed errors. These estimators are found to have small negative biases for small sample sizes and to be robust with respect to heteroscedasticity. ii Contents Abstract ii Table of Contents iii List of Tables vi List of Figures vii Acknowledgment viii 1 Statistical Sampling in the Forest Industry i n Brit ish Columbia l 1.1 The Forest Industry in British Columbia 1 1.2 Weigh Scaling 2 1.3 Statistical Estimation of Total Volume 6 1.4 Implementation Problems 9 1.5 Problem of Interest 11 2 Estimating a Population Total via Ratio Estimators 14 2.1 Estimating a Population Total 15 2.2 Some Ratio Estimators 17 2.3 Review of Empirical Studies of Ratio Estimators 21 2.4 Estimation of the Variance of the Classical Ratio Estimator of a Population Total . . . 22 2.5 Summary 29 iii 3 Jackknif ing in Ratio Estimation 30 3.1 Introduction to Jackknife 31 3.2 Jackknife in Linear Models 33 3.3 Weighted Jackknife in Linear Models 37 3.3.1 Approach Using Influence Functions 37 3.3.2 Approach Using the Fisher Information Matrix 41 3.4 Jackknife Variance Estimates in a Heteroscedastic Linear Model 42 3.4.1 Influence Function-Based Jackknife 44 3.4.2 Information Matrix-Based Jackknife 46 3.5 Weighted Jackknife Variance Estimates in Ratio Estimation 48 3.5.1 Influence Function-Based Approach 48 3.5.2 Information Matrix-Based Approach 50 3.6 Summary 51 4 Bootstrapping i n Ratio Estimation 52 4.1 Introduction 52 4.2 Bootstrap Variance Estimates - A Design-Based Approach 56 4.3 Bootstrap Variance Estimates - A Model-Based Approach 57 4.4 Weighted Bootstrap Variance Estimates 61 4.5 Some Comments on Proposed Estimators 63 4.6 Summary 65 iv 5 Sensitivity of Variance Estimates: A n Empir ica l Study 66 5.1 Purpose of Empirical Study 66 5.2 Overview of Empirical Study v 67 5.3 Description of Populations 70 5.4 Results from Real Populations 76 5.5 Results from Artificial Populations 79 5.6 Summary 82 6 Conclusions and Recommendations 84 Bibliography 87 v List of Tables Table 1. Empirical Results of Variance Estimators: Population A 91 Table 2. Empirical Results of Variance Estimators: Population B 92 Table 3. Empirical Results of Variance Estimators: Population C 93 Table 4. Empirical Results of Variance Estimators: Population Y00 94 Table 5. Empirical Results of Variance Estimators: Population Y10 95 Table 6. Empirical Results of Variance Estimators: Population Y20 96 Table 7. Empirical Results of Variance Estimators: Population Y01 97 Table 8. Empirical Results of Variance Estimators: Population Y l l 98 Table 9. Empirical Results of Variance Estimators: Population Y21 99 vi List of Figures Figure la. Populations 1-9 from B.C. Ministry of Forests 100 Figure lb. Populations 10-18 from B.C. Ministry of Forests 101 Figure 2. Scatterplot of Population A 102 Figure 3. Scatterplot of Population B 103 Figure 4. Scatterplot of Population C 104 vii Acknowledgment I would like to thank the Faculty of the Department of Statistics at the University of British Columbia for all their help. In particular, I wish to express my gratitude and indebtedness to my thesis supervisors, Dr. Mohan Delampady and Dr. A. John Petkau, for their generosity of time and effort during the research and writing of this thesis. In many ways, this thesis is as much theirs as it is mine, particularly any mistakes, errors and omissions. I also take this opportunity to thank Dr. A. John Petkau and Dr. James V. Zidek for their guidance, help and inspiration during my studies at the University of British Columbia. I wish to thank Mr. Patrick Plunkett of the British Columbia Ministry of Forests for his interest in the research of this thesis, and for providing the data which formed the basis of Chapter 5. I also wish to thank Ms. Rabiya Alimohamed for her impeccable typing and for her tolerance of numerous revisions of this thesis. It is my pleasure to acknowledge the kindness and assistance extended to me by my colleagues at Price Waterhouse. Mr. Robert G. Elton and Mr. R. Martin Roberts took interest in my studies and provided considerable financial and other support, and I take this opportunity to express my deep and sincere gratitude to them for their kindness and generosity. Finally, I would especially like to thank my friend and colleague Dr. Edward J. Mansfield for kindly and generously providing me with his time and guidance both during my studies and during my association with Price Waterhouse. He, along with Dr. A. John Petkau, has significantly contributed to my training as a statistician, and I take this opportunity to express my deep and heartfelt gratitude to them. I hope this thesis is worthy of their support and efforts. viii Chapter 1 Statistical Sampling i n the Forest Industry i n Brit ish Columbia 1.1 The Forest Industry in British Columbia Forestry is the dominant industry in British Columbia, accounting for a major portion of the provincial economic output. The extensive forests which cover much of the province form the basis for this industry. Most of the forested lands are owned by the Government of British Columbia, the management and regulation of these lands being the responsibility of the provincial Ministry of Forests. While the Ministry takes an active interest in all aspects of forestry, it does not engage in the actual harvesting and processing of timber. Rather the Ministry (under several different programs) allows private companies to do this in exchange for payments called stumpage fees. The method of calculating stumpage fees changes from time to time and can be quite complex, incorporating such factors as silviculture costs, harvesting costs, the species of tree being harvested and the estimated value of the products produced from the harvested timber. However, the central idea behind all of these methods is that the stumpage fees that a private company pays should be proportional to the volume of timber which that company harvests. Consequently, it is extremely important to both the Ministry and the companies that the volume of timber harvested be determined accurately and efficiently. Because of the differences in timber species and harvesting methods, the forest industry in British Columbia is grouped into two geographic categories: Coast and Interior. In the coast region, the Government requires that the volume of each log be measured separately. The process of measuring the volume of a log is done manually and is known as hand or stick scaling. It is 1 expensive to hand scale each log; however, coastal timber has a high dollar value and so the costs of scaling are relatively small compared to the value of the logs. In the interior of the province, timber is of lower quality and dollar value. Consequently, the government does not require the scaling of each log. Rather, all logs are weighed, and the total volume harvested by a company is estimated via a sample of logs which are both weighed and hand scaled. This process is known as weigh scaling. Regardless of the method of scaling, however, the estimate of total volume must be accurate to a level set by provincial legislation. It is one aspect of weigh scaling which motivates this study, namely that of estimating the total volume of timber harvested by a logging company to within a prescribed level of precision, while using the minimum of resources (i.e. lowest cost). In particular, this study will concentrate on the estimation of the variance of that total volume estimate. This chapter will describe the physical process involved in the logging and scaling of timber and the current method of estimation of total volume used in weigh scaling. Both statistical and non-statistical problems which arise in the process will also be discussed. 1.2 Weigh Scaling In this section, we will describe the physical process involved in the logging and weigh scaling of harvested timber. In particular, we will concentrate on the sampling aspects of this process. At a logging site, trees are cut and their branches are removed until just the trunks remain. These logs are then trimmed at the ends. Thereafter, the logs are transported to a central location within the logging area. This process, from the actual cutting to the transport to a central location is called falling, bucking and yarding. Once at this central location, the logs are sorted, tagged 2 and loaded onto trucks for transport to various locations. Typically, the majority of logs from a logging site are sent to one location. For example, this could be a pulp mill or a sawmill. When logging trucks arrive at a final destination, they are weighed both loaded and empty, so that the weight of the load may be obtained. Some statistically relevant decisions are made at the weigh scale and thus, the sequence of events occurring when a truck arrives to be weighed will be described. Before proceeding, it is important to mention that in order to reduce the amount of sampling required the Ministry of Forests allows companies to create strata. The creation of appropriate and homogeneous strata from the population of all truckloads which arrive at a specific location may significantly reduce the sample size requirement. Thus, by creating strata a company can reduce the amount of scaling it must conduct and pay for, without reducing the accuracy of the estimates. The quantity and type of strata are determined by the company, which submits its plan to the Ministry for approval. The strata are not actual physical locations, but rather, species and grade types. The Ministry determines the sampling rate for each stratum and it is the company's responsibility to ensure that it is carried out. When a logging truck arrives at a mill, it is directed to a weigh scale and weighed. The weigh scale operator, a company employee, classifies the truckload of logs as belonging to one of that company's strata. His decision is based on many factors, including the timbermark, the species makeup of the load, the amount of rot and the presence and extent of pest damage. Once the load is assigned to a stratum (or its appropriate stratum identified depending on one's point of view), the relevant details of the load are entered on a computer attached to the weigh scaling machine. The computer keeps a record of the accumulated number of truckloads belonging to each stratum. Each stratum has a different sampling rate, and the computer signals the weigh scale operator 3 when a truckload belonging to a particular stratum has to be sampled. If it is to be sampled, the truck is sent to a special site on the location where the logs are unloaded and are subsequently scaled, i.e. the volume of each of the logs is determined by hand scaling. Otherwise the truck is sent to the yard where it unloads its logs together with others belonging to the same stratum. The overall sampling process is as follows. At the beginning of the year, each logging company identifies the strata it has chosen and the anticipated number of truckloads to be harvested from each of these strata. Companies are free to choose the number and type of strata. Since a company pays for the scaling of its wood, it is in the best interest of the company to choose that combination of number and type of strata which minimizes the amount of sampling required. The Ministry of Forests then determines the number of truckloads that must be sampled from a stratum in order for a prescribed level of precision to be met. Once a sampling rate has been decided by the Ministry, it is the responsibility of the company and its employees to ensure that this rate is achieved. This prescribed level of precision is set by provincial legislation. Under provincial legislation, the estimate of total volume of wood harvested by a company must be within 1% of the true value, with a confidence level of 95%. In other words, the estimate of total volume must be within 1% of the true value, in 19 out of 20 estimates of total volume of different populations. The Ministry of Forests estimates the total sample size required from each stratum of a company in order that the overall 1% requirement be met. Since a company may have chosen to categorize their harvest into several strata, the Ministry of Forests allocates to each stratum the number of samples which are required from it. The samples are selected via a sampling scheme called block sampling: the random selection of one truck from every block of consecutive Nm trucks from stratum m, where Nm is determined by the Ministry of Forests. 4 For example, a company may choose to establish three strata and anticipate harvesting 1000, 500 and 200 truckloads per respective stratum. Using variance estimates from similar strata from the previous year, the Ministry of Forests may initially estimate that 100 truckloads should be sampled from the first stratum, 50 from the second, and 20 from the third. (These numbers are just examples to illustrate the sampling scheme.) However, since the variance estimate formula used by the Ministry is based on the assumption that the sample sizes will be large, the Ministry has imposed a minimum sample size constraint of 30 truckloads. In addition, the Ministry may at its discretion, increase the sample size if it feels the initial estimates are too small. Suppose in this example that the Ministry chose to increase each sample size by five, so that the final sample sizes per stratum would be: 105, 55 and 35. This means that for stratum one, one truck will be randomly selected from every successive set of nine trucks (1,000 divided by 105 is approximately nine) and similarly for the other strata. In other words, a block size is determined within which one truck will be chosen using simple random sampling. To maintain the integrity of the sampling process, the company is not to know which truckload will be sampled. To achieve this, the sampling scheme for each stratum is entered onto the computer at the weigh scale, and the computer generates the necessary random numbers to accomplish the random selection of one truck within each block for each stratum. The hand scaling of logs in the sample loads is carried out by specially trained employees of the company called scalers. The volume measurements obtained from hand scaling are themselves inexact as logs are often curved and bent, and may contain knots and other irregularities. Consequently, if two different scalers measure the same load, they might arrive at two different volume estimates. However, if both scalers are experienced, their estimates should not differ by a large amount. The entire process relies on trust and honesty, but there are occasional spot checks by the Ministry to ensure the scalers do not cheat on their estimates. 5 At the end of a calendar year, the Ministry uses the weights and volumes of the sampled loads to develop a set of conversion factors relating volume to weight. The estimates of total volume harvested are then made by multiplying the total weight of all truckloads by the appropriate conversion factors. 1.3 Statistical Estimation of Total Volume The previous section provided a brief introduction to the actual physical process whereby a standing tree in the forest is felled, yarded, weighed and possibly scaled. In this section we will describe the currently used analytic formulae to estimate the ratio of conversion from total weight to total volume, the total volume, the variances of the ratios and totals, and other related quantities. All the formulae are derived and discussed in the standard sampling text, Cochran (1977). For each stratum, the Ministry first estimates the total volume of harvested timber, and then estimates the variability of this estimate. To properly discuss the above statistical estimates, we will first need to establish the notation. Without loss of generality, we will consider the population of interest to be a particular stratum, the basic unit of which is a truckload. Let N be the total number of truckloads of timber harvested from a particular stratum during a calendar year, and let n be the number of truckloads sampled during the year from that stratum. Let Y be the total volume of timber in a population, Y = jryi = NY, i=l and let X be the total weight of timber in that population, N _ X=\"£Xi=NX . 6 Let R be the population ratio of volume to weight (inverse of the population density), R - - I - Z , x X and let f be the sampling fraction, N The mean volume of the scaled (sampled) truckloads is y = - i > i > where yt is the volume of the ith sampled truckload. Similarly, the mean weight of the sampled truckloads is x=—£ *« • where xt is the weight of the i'h sampled truckload. The estimate of the population ratio of volume to weight is X and the corresponding estimate of the population total volume is Y = AX= 2.X where X is the total x weight of all truckloads. A large-sample approximation to the variance of the ratio estimate is Var(R~) = 1-f ynX2 J ( 1 \\SL 7 and, since the population quantity X is known, the natural sample analogue of this is va, ritt) r \\ 1-f KnX2, f i \\ \" 1 ' E^ - a* , ) 2 n - l Since Y = llX = ANX, the corresponding large-sample approximation to the variance of the estimate of the population total is VariY) * v n J N2 N-l for which the sample analogue is va n-l £i To determine the total sample size required for a company, the Ministry of Forests uses \"estimates\" of the required population parameters. These \"estimates\" are really anticipatory figures, possibly being based on previous year's data or from a few samples at the beginning of the year. After determining the total sample size required for all strata, sample sizes are determined for each individual stratum by Neyman allocation (Cochran, 1977, p. 99). If the estimated volumes and standard deviations required to implement Neyman allocation are not available, then the total sample size is distributed to the individual strata based on the anticipated number of loads to be harvested from each stratum (proportional allocation). However, because the variance estimates are based on large sample approximations, all sample sizes which are less than 30 are increased to at least 30, and often, to 35. Thus, there is an incentive for companies to select large strata; otherwise they will be required to sample more truckloads (and incur the additional costs associated with scaling these truckloads) so that the variance estimates are accurate. 8 The total volume harvested by a company is estimated by summing the estimates of the total volume for the m strata, m Ul and the variance estimates from all strata are added to obtain the variance estimate of the total volume: vari?) = jtvartfi) ui The above are the analytic formulae used by the Ministry of Forests. In the next section we will discuss both statistical and non-statistical problems associated with the presently used scaling system. 1.4 Implementation Problems Several practical, implementation type problems arise as a result of difficulties in properly applying the required methods, whereas other problems arise simply because the underlying statistical theory is not correct for the problem at hand. Though this research deals with the statistical theory upon which the scaling system is based, it is nevertheless important to understand some of the implementation issues, simply because the presence of inconsistencies between theory and practice reduces the effectiveness of the scaling system. The implementation problems can be categorized into two types: system errors and intentional errors. Intentional errors are those errors in sampling, weighing or data collection which are either committed on purpose or allowed to go uncorrected. The Ministry of Forests collects over $500 million in stumpage fees annually. The sheer magnitude of this figure implies that there may be some groups who will try to reduce the amount they are required to pay. However, these 9 are legal rather than statistical problems. Consequently, for the purposes of this research, we will not consider these problems. System errors are those errors arising from the actual implementation of the scaling system. These errors can be classified into three general categories: misclassification errors, measurement errors and random sampling errors. An alternate way of viewing these errors is: errors due to measuring what should not be measured, errors due to mistakes in measuring what should be measured and the errors arising from having a sample which does not adequately represent the true population. Misclassification errors are those errors due to misclassifying a truckload of timber into the wrong stratum. Recall that when a truck arrives at a mill, it is assigned to a stratum depending the geographic source of the logs, the species and age mix, amount of rot, amount of pest damage and other criteria. However, this decision is subject to human error. For example, a truckload of mostly hemlock and a bit of fir may be misclassified as belonging to a stratum containing fir only. Outliers in sample data could be due to measurement errors, to misclassification errors, to other errors or they might be a feature of the population. Therefore, it is difficult to ascertain whether misclassification errors constitute a major source of variability. One method of determining the rate of misclassification errors is to search for outliers in the sample data, although this would involve an implicit assumption that outliers within a stratum are due to misclassification errors and are not simply a feature of the population. Measurement errors are those errors arising primarily from inaccuracies due to the measuring procedure used by scalers in measuring the volume of a truckload and from the errors due to weighing a truckload. As mentioned earlier, two scalers measuring a load of timber will occasionally produce volume estimates with substantial differences. However, the Ministry staff 10 feel that scaler to scaler differences do not constitute a major source of variability. Consequently, we will disregard scaler to scaler error. The errors due to weigh scales are not likely to be substantial when considered in relation to other sources of error. The weigh scales are electronic and are (presumably) regularly calibrated. Thus, it is quite likely that scaler to scaler variability exceeds weigh scale error, and if we are disregarding the former, we may also disregard the latter. It should be noted that these sources of error are not ignored; we will simply make no attempt to isolate them. All these sources of error are aggregated and considered as the sample to sample variability in the ratio. Random selection errors are those errors which cause an improper sample selection - aside from intentional \"errors\". A primary source of errors here are the anticipatory figures used at the beginning of the year to determine the sample size breakdown between the strata. For example, a company may anticipate harvesting a certain number of truckloads from a stratum, and at midyear may realize they have overestimated - that in fact they will harvest fewer truckloads from this stratum. Then, during mid-year updates, the Ministry may reduce the sampling rate for that stratum for the rest of that year. Consequently, the wood harvested during the latter half of the year would be under-represented in the sample from that stratum. And since there often exists a difference between the type of wood harvested during the summer and winter seasons, this may turn out to be a major source of error. Another problem is that truckloads may not arrive at a weigh scale in random order. In other words, the semi-systematic method of sampling used may not be as good as a true simple random sample. As Kish and Frankel (1974) note, \"'well-mixed' urns are seldom provided by nature or created by man\". 1.5 Problem of Interest In this thesis, we will concentrate on the estimation of the variance of the estimate of total volume. Recall that in the current method of estimating the variance of an estimate of total volume for a 11 stratum, the Ministry requires that the sample size exceed a minimum of 30 (see Cochran, 1977, page 153). One question we will address is whether the variance estimator we described earlier is accurate when the sample size is less than 30. We will also investigate alternate estimators of the variance, particularly those estimators which perform well at sample sizes less than 30. We believe that identification of an effective small sample estimator of the variance would benefit both the forest companies of British Columbia and the B.C. Ministry of Forests. The benefit would arise because the minimum sample size requirement could be reduced thereby allowing companies to select smaller and possibly more homogenous strata. Such strata would require fewer samples than larger, more heterogeneous strata and the overall result would be greater efficiency in the weigh scaling process. The primary motivation for our thesis is the possibility that recently developed resampling techniques might provide accurate and effective variance estimators for small samples. These techniques (technically known as the Bootstrap and the Jackknife) have not been studied in detail in similar problems, but previous empirical studies suggest that these methods might be effective in our situation. Consequently, we will derive estimators based on these new techniques and we will study their small sample behaviour via an empirical study. In the next chapter, we will review various methods of both estimating the population total volume and of estimating the variance of these estimates. This will be done in a general statistical context. Thereafter, in Chapter 3, we will derive weighted jackknife-based variance estimators. As one author has pointed out, \"the weighted jackknife...would seem to be particularly appropriate for sample survey applications\" (Smith, 1981). In Chapter 4 we will continue with our derivation of resampling-based estimators, particularly in the context of ratio estimation. It has been suggested that bootstrap techniques are more accurate than jackknife techniques (Efron, 1979), and thus this chapter will attempt to derive bootstrap analogues to the estimators obtained in 12 Chapter 3. In Chapter 5, we will conduct empirical studies on the performance of the various variance estimators in the context of the types of populations faced by the Ministry of Forests. In addition, we will also study the robustness of the various estimators, particularly with respect to common model assumptions. Finally, in Chapter 6, we will summarize the results of this study and provide specific recommendations concerning the statistical aspects of the weigh scaling process currently used by the Ministry of Forests. 13 Chapter 2 Estimating a Population Total via Ratio Estimators In Chapter 1, we described and discussed the implementation of the weigh scaling program by the B.C. Ministry of Forests. In this chapter, we will discuss weigh scaling from a statistical point of view, concentrating both on the estimation of total volume via ratio estimators and on the estimation of the variability of these estimators. The weigh scaling program may be statistically represented as the following estimation problem: A finite population consists of N units of two associated variables of interest: =(Xi,Y'i), i=l,2,...^ V iV N with corresponding population totals Y = ]jT Y, and X = Xt. In our context, the units of the i=l i=l population are the truckloads and the variables Xt, Y, are, respectively, the weight and volume of the i'h truckload. Having observed a simple random sample of n pairs of Xit Yi we wish to estimate both the total volume Y and the variance of our estimate of total volume, Var(Y), given that we know X. In this thesis we will focus on the variance estimation problem given that a particular approach will be taken for the estimation of the population total. That particular approach is to estimate the population total volume, Y, by the classical ratio estimator YR =ficX= Z -X. x However, there are other possible estimators of Y, and we will briefly discuss some of these before proceeding to our main topic of interest: the various proposed estimators of Var(YR). Our reason 14 for discussing alternate estimators of Y besides YR, is mainly to place the variance estimation problem in context. The problem we will attempt to address is a small part of a much larger problem: the problem of drawing inference on Y given a sample of pairs of Xit Yt and given fairly complete information on the covariates XU...JCN. 2.1 Estimating a Population Total There are two common approaches for estimating Y: One approach ignores covariate information, while the other takes advantage of this information. The simplest estimator of the former approach, known as the mean-per-unit method, first estimates the population average of Yt, and then multiplies it by N. In our context, this amounts to first estimating the average volume per truckload, and then multiplying this by the total number of truckloads. We will denote this estimator by YM = 7Vy, where y is the average of the observed y1,...Jyn. The alternate approach is to make use of covariate information. There are many ways of making use of covariate information, the simplest of which is as follows: YR = 2Lx. For obvious reasons, x this is known as the ratio method. In our context, this amounts to first estimating the inverse of the average density and then multiplying this by the total weight, X, to estimate total volume. Quite often, a model-based approach is used for estimating a population total. Under this approach, the expectation of Yt is assumed to be a function of Xt. Consequently, it is natural to estimate Y using our knowledge of Xt's. The model typically used is a linear model with specific variance assumptions: 15 Yt-Xtt+et, (2.1) where the e( are the stochastic components. The e, are assumed to have expectation zero, and a common assumption is that their variance given xi is a 2*,. If we assume that the ei are normally distributed, then the maximum likelihood estimator of P is the classical ratio estimator Rc = L. x Even without the normality assumption, weighted least squares results in p1 =fic. An appropriate question to ask here is: under what circumstances is YM a better estimator of Y than is YH? It can be shown using the design-based approach (Cochran, 1977, p. 157) that for simple random sampling with large samples, the ratio estimate YR has a smaller variance than if 1 r \\ Y 2 K v y J where Sx, Sy are the standard deviations in the X and Y populations respectively, and pXJ, is the correlation between Xt and Yt in the population. For the total volume estimation problem of the B.C. Ministry of Forests, the ratio estimator would be expected to be the better of the two estimators because, in general, the correlation .between Xt and Yi is large, while the two coefficients of variation are similar. Hence, we will concentrate on 16 ratio type estimators of the population total: Yt = R,X, where the . indicates a particular method Y y of estimating R = = X x 2.2 Some Ratio Estimators Ratio estimators have been studied for several decades. As early as 1932, a non-symmetric confidence interval for R =E(Yi)/E(Xi) was being studied under an infinite population and bivariate normal assumption (Fieller, 1932). During the 1950's, numerous researchers approached the ratio estimation problem through the design-based framework. In this section, we will briefly discuss several ratio estimators. The most commonly used method of estimating R is by the classical ratio estimator, #c which is given by the ratio of the average of the observed Yt's divided by the average of the observed X/s: — 1 \" 1 \" &c = = , where y = _ £ yt and x = _ £ xt . x n ; = i n i = i It has been shown that the ratio of the averages is a better estimator of R than the average of the ratios. If we assume that Var(Yi)'xXi and that the relationship of Y( and Xt is a straight line through the origin, then it can easily be shown (by using Lagrange Multipliers) that the estimate of the population total obtained via the classical ratio estimator is the best linear unbiased estimator of the population total under the design-based approach (Cochran, 1977, p. 158); Brewer (1963) and Royall (1970) show this for finite populations. Under the model-based approach, the result is of course, well-known from regression theory. 17 However, the classical ratio estimator is biased. Using an alternate approach, a design-unbiased estimator of R was obtained by Hartley and Ross (1954). Their estimator is obtained by correcting - 1 \" for the bias in the average of the ratios: r = _ JTJ rit where r^yjx^ Note that: N J _ £ ri(xi-X) = Y-XE(ri)=X[R-E(ri)] N ui Thus, the bias in 7 can be expressed as E(r)-R=E(ri)-R = --l=T ^M-X) NXt-i (2.2) By Theorem 2.3 of Cochran (1977), it can easily be seen that an unbiased estimate of the above bias is ( \\ yNX; ra-1 ui ( \\ yNX; n-1 (2.3) Then by substituting (2.3) into (2.2), E{?)-R=E' -1 (7V-l)n r- — v NX n-1 Therefore &HR = r + -^L (y - r x ) is an unbiased estimator of R. NX (n-1) In a similar attempt, Mickey (1959) also derived a design-unbiased estimator of the ratio. His estimator was obtained in an effort to find a class of unbiased ratio and regression estimators and 18 is derived using a \"leave-one-out\" approach. By estimating and correcting for the bias in average of the \"leave-one-out\" estimates of R, n ui nx -xt n i,\\ Mickey obtains the following design-unbiased estimator of R: RM = R . - { N - n t 1 ) n [ y - ^ . x ) . Tin (1965) took a different approach to correcting for the bias in Rc. Note that Rc = l = l-R-i.R=b!Zl-R •R Y X = R 1 + 1^X1 Y -_x) ^ - x ) \\ x x2 = R w ( y -Y) _(x-X) _ x2\\ Using a general bias-reducing technique known as the jackknife (which is discussed in more detail in Chapter 3), Durbin (1959) obtained the following estimator of R: ' n - i Rj = nRc- R.. Durbin was able to show that under some distributional assumptions, the jackknife method leads to estimators which are less biased and more efficient than the classical estimator Rc. Rao and Webster (1966) showed that the above \"leave-one-out\" method is the optimal version of the jackknife for ratio estimation. 20 Whereas Mickey's method estimates the overall bias with explicit use of covariate information, the jackknife method estimates and attempts to eliminate first order bias without the direct use of covariates. Although both Mickey's estimator and the jackknife estimator use the two methods arise from different approaches. Mickey's estimator arises out of a design-based, general methodology to produce unbiased ratio and regression estimators. On the other hand, the jackknife is a general, all-purpose technique designed to eliminate or reduce the bias of an estimator. 2.3 Review of Empirical Studies of Ratio Estimators Numerous empirical studies have been conducted on the preceding estimators of R. An empirical study by Hutchison (1971) showed that if X;'s are simulated as log normal, and if the errors are simulated as normal with zero mean and variance proportional to o2xx, where X = (0, 0.5,1,1.5, 2), then the Hartley-Ross estimator is as efficient as any of the other estimators. If the XSs are simulated to have a gamma distribution, Rao and Rao (1971) conclude that Mickey's unbiased estimator is more efficient than the Hartley-Ross estimator; this is especially so when X = 0. Hutchison (1971) and Rao and Rao (1971) show that though Mickey's estimator is preferable to the Hartley-Ross estimator, it is not as good as some other estimators; in particular, the Beale, Tin and jackknife estimators. The jackknife estimator has been studied on numerous occasions and the general conclusion is that it is a very good estimator (Durbin, 1959; Rao and Webster, 1966; P.S.R.S. Rao, 1969; Rao and Rao, 1971; Hutchison, 1971; and, Rao, 1979). Another pair of estimators which have been shown in the above studies to be accurate and efficient are Tin's and Beale's estimators. Both have bias of order n 2 and have estimated variances comparable to the estimated variance of the jackknife estimator. 21 It is clear from the above discussion that there are numerous estimators of the ratio. But our objective in this thesis is to consider estimation of the variance in estimating a population total using some form of the ratio estimator. To keep the problem to a manageable scale, we shall restrict our attention to the classical estimate of the ratio, fic. Henceforth, all references to R and Y will imply estimation via the classical ratio estimator method. The techniques we shall use in this thesis to develop new variance estimators for the estimate of population total using the classical ratio method could also be applied to develop new variance estimators for other methods of ratio estimation. 2.4 Estimation of the Variance of the Classical Ratio Estimator of a Population Total The problem to be addressed in this thesis is the estimation of the variance of the estimator of total volume given by: t=RX=lX. x This estimate is simply an estimate of R scaled by X, and thus, Var(Y) = X2Var(R) = (NX )2Var(R). It can easily be shown that the approximate variance of # under simple random sampling is: Var(R)± ( \\ KnX% ^(Y.-RX^ N - l This approximation has a bias of order n_1. The exact variance of i£ cannot be evaluated under the design-based approach because both X{ and Yt are random; the difficulty arises because R contains random variables in both the numerator and the denominator. 22 Therefore the approximate variance of Y (with a bias of order n 1 ) is: Var(Y)±(NX)2 ( \\ 1-f y n X \\ £ (Yt-RXy N-l rN*a-f) n 5: ( y , - / ? ^ N-l By estimating the sum of the squared residuals, the natural sample analogue of Var(R) is var. 1-f KnX\\ and therefore, vn = far , I n v J In some ratio estimation contexts, X is not known and therefore not available for the evaluation of var0(li). Estimation of X by x leads to the alternate estimator of Var(R): varAR) = n-l which in turn leads to 23 i>, = var. fN2a-f) n X 2 f \\ Ul n-1 Results from empirical studies on the performance of u0 and v2 as estimators of MSE$) are mixed. In terms of squared error, some studies suggest that v0 is a better estimator (e.g., Krewski and Chakrabarty, 1981), whereas other studies suggest that v2 is a better estimator (e.g., Wu and Deng, 1983). It is difficult to ascertain which estimator performs better. However, it is thought that both methods seriously underestimate the MSE(R) for smaller sample sizes, with relative bias on the order of -15% (Cochran, 1977; Rao, 1968). Recall that under the assumption of a linear model where the variance of Yt given xt is a2xt, /3 is the weighted least squares estimator of P . Assuming the linear model of (2.1) and assuming that the population values ylt...yN are realized values of random variables Ylt...,YN, one obtains as the variance of the usual estimate of population total (Royall, 1970; Royall and Eberhardt, 1975): VarmJY) = a2 fN2(l-f) NX-nx N-n (2.4) An unbiased estimator of varmJY) is var^CY) = d 2 fN2(l-f)) v n J r \\ x ( ^ NX-nx N-n (2.5) where &2 = J-±(yi-Rxi)2xi-1. n-1 ui 24 (This linear model approach towards finite population sampling is known as the prediction theory approach. For more details, see Royall, 1970, 1971). This estimate is biased under violations of the variance assumption (Royall and Eberhardt, 1975). The empirical study of Royall and Cumberland (1981) suggests that this estimate of variance consistently and significantly underestimates the variance of the total volume estimate. Under the prediction theory approach (where Yt's are random and xt's are not), Royall (1971) has shown that E(vQ) = o2 OV 2( l-/)^ V n J l-Iv,2 n x , (2.6) where v2 = J-Y,(xi-x )2x-2 n-l ui and the expectation is with respect to the underlying distribution of the random Yt's, and not the distribution imposed by the sampling design. Note that whereas (2.4) is a decreasing function of the sample mean x, (2.6) is an increasing function of x. This may explain the poor performance of (2.5) in Royall and Cumberland's (1981) empirical study. Under the prediction theory approach, v0 is biased and the following estimator removes its bias (Royal and Eberhardt, 1975): varH(Y) = var0(Y) ( \\ X KX J ( NX-nx N-n f i V i i-lvx n 25 However, varH(Y) is biased (under the prediction theory approach) under violations of the variance assumption. In an attempt to robustify varH{Y) to these violations, Royall and Cumberland (1978) suggest a new estimator which is approximately unbiased for different assumptions of variance: varD(Y)- N2(l-f) v n j V * , V _ V A (y^Rx,)2 L> 1 v n x J NX-nx N-n The empirical study of Royall and Cumberland suggests that varD(Y) has a lower bias than varH(Y), at least for the populations contained in their study. Wu and Deng's (1983) study suggests that varH(Y) has a lower root mean squared error than varD(Y). Both these studies present results for the same sample sizes (n=32) and for the same populations. Thus, it appears that the bias robustness of varD(Y) is achieved at the expense of increased instability. Wu (1982) suggested a new estimator which is the geometric mean of var0(Y) and var2(Y); that is: { n J ( _ \\ X V 5 J E (Vi-Rx^ V n-1 Note that the only difference between v0, vl and v2 is in the value of the exponent of (X / x ). Extending this, Wu (1982) suggested using the data to estimate the optimal value of this exponent; optimal in the sense of minimizing the leading terms in the asymptotic expansion of the mean 26 squared error of v. = ( V X •var0. The sample analogue is g as described below. For more detail, see Wu (1982) and Wu and Deng (1983). This new estimator is f var. 2 Q ^ n ) ( V n - l where g = sample regression coefficient of -1 on -J. , z x E ^ - ^ O 2 i i = ( y i - « x j ) 2 - 2 ( y i - ^ i ) - ± i , and n i = i A similar estimator suggested by Wu and Deng (1983) is obtained by removing the second term in the above zt; then zt is simply the squared residual (y^fiX^2. This leads to: var.{Y) < J ( _ V X V * J n - l where g = sample regression coefficient of ( y i - * * < ) 2 on _ 2 a: n i , i The empirical study by Wu and Deng (1983) suggests that var AY) is not as good an estimator as var, 27 Fuller (1981) suggested a new estimator which was motivated by the following observation: If the variance of et is a function of xt, then ef is correlated with (x, -X). Thus, Fuller suggests that because the population mean of the x's, X, is known, the parameter to estimate should be the variance of e, conditional on X. A linear approximation of the conditional variance leads to the following estimator of the variance of Y: Wu and Deng's (1983) empirical study suggests that this is a very good estimator. The jackknife method, which was briefly mentioned in Section 2.2, also provides an estimate of variance of the \"leave-one-out\" estimates of the ratio. (This will be discussed in more detail in Chapter 3.) The jackknife estimate of variance is: Both the empirical studies by Royall and Cumberland (1981) and by Wu and Deng (1983) show that the jackknife variance estimate performs very well. = var0(Y)+N6{X -x) , where 8 = sample regression coefficient of (y^ttx^2 on x, . variance. The idea here is to estimate the variance of 1? by the appropriately scaled sample 28 2.5 Summary There are numerous estimators of both the ratio and of the variance of the classical ratio estimator. These estimators are derived from certain assumptions about the type of situations in which the estimators will be applied. For example, Wu's variance estimators assume that the variance of et is a2xt and that the sample sizes are large enough to ensure approximate validity of the asymptotic expansions. The success of these estimators in any given situation depends upon the particulars of that problem. Because we are interested in applying these estimators in situations with a variety of population characteristics, it may be that resampling-based estimators will perform best over all the populations. Consequently, in the next two chapters we will concentrate upon deriving resampling-based estimators of the variance of the classical ratio estimator. 29 Chapter 3 Jackknif ing i n Ratio Estimation In this chapter, we will concentrate on jackknife variance estimators for the classical ratio estimator. Empirical studies suggest that the jackknife variance estimator presented in Chapter 2 performs reasonably well in estimating the true variance of a ratio estimate. Recently, variations on the jackknife method have been proposed; the new versions are commonly known as weighted jackknife methods. We conjecture that these methods will lead to better variance estimators in ratio estimation. In Section 1, we will briefly discuss the (unweighted) jackknife. In Section 2, using linear models at first, we will show how and why the unweighted jackknife estimator does not perform well in the presence of imbalance; especially when the linear model, which is already unbalanced, is further perturbed by heteroscedasticity. Linear models will be used because they include a wide range of models, including the commonly used model for ratio estimation (already discussed in Chapter 2), and because they exhibit imbalance of a type which is probably similar to the imbalance in ratio estimation. The imbalance referred to is the absence of identical distributions. That is, the observations have different means and sometimes, different variances. After discussing the problems arising from the use of the jackknife in unbalanced problems, we will, in Section 3, discuss the weighted jackknife which attempts to reduce some of the difficulties which arise due to imbalance. We will review and expand upon the weighted jackknife variance estimators for linear models suggested in Hinkley (1977) and Wu (1986). Thereafter, in Section 4, we will derive the weighted jackknife estimators for the special case of the linear model discussed in Chapter 2. Finally, in Section 5, we will derive weighted jackknife variance estimators for the 30 ratio estimator based on the approaches of Hinkley (1977) and Wu (1986). We will derive three weighted jackknife variance estimators, two of which will be shown to be equivalent to common design-based variance estimators. 3.1 Introduction to Jackknife The jackknife estimator of the ratio, fij, was described in Chapter 2 along with the jackknife estimator of the variance of the classical ratio estimator. In this section, we will present a more thorough and formal description of jackknife estimators. Let G be the parameter of interest where 0 = 9 (F) and F is the distribution of the random variable Z. Suppose we have an estimator of 9 based on a random sample zv...,zN denoted by 0(z1,...,zn) = 6 . Let be the estimate of 9 based on the sample with the i\"1 observation removed: = &(zv...jzi_1jzitV...j!n). Then the pseudovalue for this \"leave-one-out\" case is defined as (Quenouille, 1956) ^ = 710-01-1)9^ . The jackknife estimator is obtained by averaging these pseudovalues: &J = P. = lJ2Pi = n§-(n-D±'E ^ = n8-(n-l)8. . In general, hj removes the order n'1 term bias (Miller, 1974a) from biased estimators having expectation of the form £(S) = 0 + _L + O n 31 n2 This form of the jackknife is often called the balanced jackknife because there is an implicit assumption that each observation has an equal effect on the parameter estimate. Thus, the parameter estimate is the unweighted average of the pseudovalues. This form of the jackknife usually works very well in situations with some natural balance. The above pseudovalues and the corresponding jackknife estimates were proposed in a more general form in Quenouille (1949, 1956). The original version dealt with breaking up n observations into g groups of size h, so that gh-n, and deleting the i'h group of observations to obtain 0 ,. The more general pseudovalues and corresponding estimator are Quenouille's original motivation for introducing the jackknife estimate was to eliminate bias of order n 1 . He also suggested similar estimates to remove higher order bias; however we will only deal with the above \"leave-one-out\" version. Tukey (1958) proposed treating the pseudovalues, Pit as independent and identically distributed random variables, thereby obtaining the jackknife estimate of the variance of S^ : ge-Oj-De., where 8. n uaK§ J) = {n(n-l)}-1E (P,-P.)(P (-P.)' , 32 The variance estimator uar,(Y) which was discussed in Chapter 2, was simply an application of the above to the classical ratio estimator, with the finite population correction term appended. In both this and the following chapters, whenever variance estimators are used in finite populations, the finite population correction term, (1-f), will be appended to those variance estimators which were originally intended for use in infinite populations. 3.2 Jackknife in Linear Models The jackknife is effectively designed for balanced problems; problems which are in a sense symmetric. The jackknife estimates are simple averages. Thus, there is an implicit assumption of balance, or symmetry. However, the model-based ratio estimation problem is not symmetric, because the observed yt's are not assumed to be identically distributed; their means, and sometimes variances, are functions of the xt's. Thus, from the model-based approach, one might not expect the jackknife to perform well in estimating the variance of the ratio estimator. In this section, we will attempt to show the inadequacy of the jackknife in the linear model setting. The linear model will be used because it has an obvious and known imbalance: the y/s are not identically distributed. An additional advantage of using the linear model is that, in a special case, the weighted least squares estimator of 3 is the ratio estimator. For applications of the weighted jackknife in non-linear regression, see Fox, Hinkley and Larntz (1980) and Simonoff and Tsai (1986). 33 In the following discussion we will use a linear model with equal variance for the observations; that is, Var(Y;) = a 2 for all i. This will be referred to as the homoscedastic case. The heteroscedastic case occurs when VariY^ = a). Unless explicitly mentioned otherwise, we will assume the following model: Y=xp+; is of order n 1 , it can be shown that is of order n'2 (Hinkley, 1977). The jackknife estimate of the variance of the jackknife estimate 3 j is the appropriately scaled sample variance of the pseudovalues; namely, var,® J) = {n(n -1)}\"1 £ (P, -3.,)CP, - 3 jY • ui Miller (1974b) and Hinkley (1978) argue that the jackknife variance estimate can be used to estimate the variance of the least squares estimator 3. We will denote uar,(3 j) by war,. Using simple algebra, it can easily be shown that the expectation of this jackknife variance estimate is (Hinkley, 1977) 36 E(varj) = the LSE's based on the \"delete-one\" samples. For the general p-dimensional case, Wu (1986) shows that Ul \\x'x I where «, = . (|-| denotes the determinant) An alternate way of viewing this representation comes from the recognition that in this representation the weights are the determinants of the Fisher Information matrices of the particular data subsets under consideration. Of course, the Fisher Information matrix depends upon distributional and model assumptions; the above weights are obtained under a homoscedastic linear model, with normally distributed errors, but this idea can be generalized under other distributional and model assumptions. Interpreting the weights ut as the relative information content of the sample with the i'h observation removed, led Wu (1986) to propose the following weighted jackknife estimate of the variance of 3 : 41 i ;or w (p )« (n - l )u i fa_ t - l ) $_ i -b ) ' . (3.5) ui In fact, Wu's proposal is more general than the above method which we have described. He considers the more general version of the jackknife which removes an arbitrary number of points, not just one as in the \"delete-one\" version. However, we will only deal with the delete-one version of Wu's variance estimator. We do this for three reasons: (i) in order to compare varw($) with varH($). Otherwise, the weights are difficult to compare because both ut and (3_;-3)(3-i-3)' change; (ii) because the robustness of the delete-one version of varw($) can be established (Shao and Wu, 1987); and, (iii) because it has been established that the delete-one version of the jackknife is optimal for estimation of the variance of the classical ratio estimator (Chakrabarty and Rao, 1968). Wu (1986) shows that varw is unbiased in the homoscedastic linear model, unlike varH which was biased. To summarize, the weighted versions of the jackknife variance estimators proposed by Hinkley (1977) and Wu (1986) eliminate or reduce the bias of the unweighted versions. Though our attention has been restricted to linear models, we may still conclude that by taking into account model imbalance, the standard jackknife can be improved in some situations. Though both these approaches have been presented only for linear models, the same approaches may be useful in more general unbalanced situations. 3.4 Jackknife Variance Estimates in a Heteroscedastic Linear Model In this section, we will explicitly derive weighted versions of the jackknife variance estimator for the linear model for regression through the origin with variance structure specified by 42 Var(c£) = a2xx. We have chosen this version of the linear model because in many practical applications of the ratio estimator, this form of variance does indeed occur; and furthermore, for the particular value of X = 1, the weighted least squares estimate of P is the classical ratio estimator. From this section onwards, we will set p=l; that is, x, and P will be one-dimensional and not vectors. The linear model with heteroscedasticity of the form Varie^ = a2x* can be converted to a homoscedastic case. Thus, the model Model 1: E(et) = 0 , Var(et) = o2x? and et independent of for i±j , may be transformed to the model y;=x*p+ e*, Model 2: E(e*) = 0 , Var(e*) = a 2 and e' independent of ef for i*j , where the transformation * simply denotes division by x'. When X = 1, the least squares estimator P of p in model 2, which corresponds to MLE in model 1 had we assumed normal errors, is easily seen to be the ratio estimate, & = . This suggests one way of obtaining x unbalanced jackknife estimates of the variance of ft: Using model 2 and X = 1, Hinkley's and Wu's 43 weighted jackknife variance estimators will be obtained. These will then correspond to model-based estimators of the variance of ft. 3.4.1 Influence Function-Based Jackknife From Section 3.3.1, Hinkley's estimate of the variance of 3 is (3.4): l;arH = _J_E(Q i-Q.)2 n{n-l) ui and since the linear model weighted pseudovalues for model 2 are given by Q, = 3 + nD^x*r*, we have n-l ui But for our transformed problem Do1 = (X\"X')-1^ E*. 2-X \\ \" J Therefore, ( \\ n-l i . 2-X. V>1 j n-l ui ( Y 2 E*/ For the case of A. = 1, this expression becomes varH(ft) = ^-±(yi-xiftf-^ n-l i (w)' 44 Therefore, varH(Y) = varH(NXll) , N2 ra(ra-l) i = i uar2(Y)/(!-/) . ( V Thus when the finite population correction factor is included, the variance expression resulting from Hinkley's version of the unbalanced jackknife is the same as Cochran's second estimate of the variance, varJY). If we re-express varH as the sum of squared delete-one estimates of the parameter, as in (3.4), we obtain f n ^ E ui 2-X \\2 2-X For the case X = 1, this becomes ( *, ^ \" varH(R)= \" E {n-l)i,i ( ^ 713t n-1 45 In Hinkley's version of the weighted jackknife, the weights are f V x : K x J , where x_t is the mean of the sub-sample obtained by deleting ith observation on x's. Thus the weights are a function of the sub-sample balance on the first moment. If the sub-sample mean is less than the sample mean, then the weights are less than one, implying that (R_t -R)2 gets down-weighted relative to the same terms corresponding to a sub-sample with high first moment. At first glance, it is surprising that subsamples with a high first order moment receive more weight. Of course under the super-population model, the larger the £;'s, the smaller is the variability of R, and perhaps this explains why subsamples with high xt's are weighted more. 3.4.2 Information Matrix-Based Jackknife We will now consider Wu's version of the weighted jackknife. From (3.5) in Section 3.3.2, Wu's weighted jackknife estimator of the variance of 3 (for p=l) is defined as varw$) = (n-l)J2 «((3-i-3)2 , i and the weights for model 2 are: V X 2 X U; - • El*:;*;I EE* 2~ X y=l >1 l*j (n - l)x • When X = 1, this becomes u, = = which leads to n(n-l)x 46 ( \\ uarw(0) = E [ n J , X J f \\2 Thus, it is seen that whereas the weights are KX J in Hinkley* s version, they are in Wu's version. In both instances, the weights are increasing functions of the \"information content\" and hence are appealing and reasonable. Even when the Fisher Information of a \"leave-one-out\" sample is not 5c_f, these weights appear to be reasonable because many studies have indicated the importance of the first moment balance (Royall and Cumberland, 1981; Wu and Deng, 1983; Royall and Cumberland, 1985; Robinson, 1987). Wu's weighted variance estimator can also be represented (using the Lemma in Chapter 2) as var, nx2 n-l u ( \\ x V k J (3.6) In the similar representation of varH(R) in Section 3.4.1, it was found that varH(ft) was equal to var2(&). For Wu's variance estimator, we see that varw(R) is not equivalent to any variance estimator which we have considered. In this heteroscedastic linear model, we have obtained two weighted jackknife variance estimators. Wu (1986) and Shao and Wu (1987) derive asymptotic results for the heteroscedastic-robustness of the weighted jackknife methods presented here. Their results deal with the more general heteroscedastic models, where the variances of the errors are not a known function of the xt's. They show that both varw($) and varH($) have order n'1 bias in the general heteroscedastic case. 47 They further show that varw($) is unbiased in the specific heteroscedastic case where X/DQ 1 xy- is equal to zero for any /,/ with a, not equal to ay. In this section, weighted versions of the jackknife variance estimator were derived for a heteroscedastic linear model. These estimators were obtained from a model-based approach to ratio estimation. In the following section, a design-based approach will be taken in deriving weighted jackknife variance estimators. 3.5 Weighted Jackknife Variance Estimates in Ratio Estimation In this section, we will explicitly derive weighted versions of the jackknife variance estimator of the classical ratio estimator. Following the approach of Hinkley (1977) and Wu (1986), we will show how Hinkley's version of the weighted jackknife variance estimator leads to the two variance estimators var0 and var2. Thereafter we will show that Wu's approach leads to the variance estimator which was discussed in Section 3.4.2. 3.5.1 Influence Function-Based Approach Recall that Hinkley's approach in deriving weighted pseudovalues employed estimated influence functions. Therefore, we will first derive the estimated influence function of R, and thereafter derive the corresponding pseudovalues and estimators. Let us define the functional R of the joint distribution G of X and Y as follows: where Fy and Fx are the respective marginal densities of Y and X. 48 If 82 represents a point mass at (Xz, Yz) then the influence function of R is defined as follows: -e)G+e8z]-fl(G) Straightforward calculation yields limit . :e-> 0 (1-e)^+ey2 u, (1 -e)\\ix+eXz u, y 2u x-u Xz (1 -e) p.,+e*znx JL Y-hx, UYz-nxy There are two sample analogues of this influence function, depending on whether we use the sample estimate of \\ix or the actual value: iF^Y,,tt) = I (Y,-RX,) or lF2(Xx,Yz;R) = ±(Y,-RX,) X The modified pseudovalues based on lFx are Q i =R+lFl (x^y^.R) = i? + 1 ( y , i = l , . . . , n , with the corresponding estimator 49 the usual estimator. The corresponding weighted variance estimator is var re (n-l) ui n(n-l) i=i lac nx2 -ww 2(#)/(l-/) . Thus, when the finite population correction factor is incorporated, the weighted estimator of the variance corresponding to lFx is var2, the second estimator suggested in Cochran (1977). Similarly, the use of lF2 results in modified pseudovalues which lead to the variance estimator var0. Thus, explicit derivation of Hinkley's version of the weighted jackknife results in two known estimators, in contrast to the results obtained in Section 3.4.1 where Hinkley's version results in just one known estimator, var2. We will now proceed on to Wu's version of the weighted jackknife. 3.5.2 Information Matrix-Based Approach Recall that Wu's approach can be motivated by the use of weights proportional to the determinant of the Fisher Information Matrix of subsamples obtained by systematically removing individual observations. The use of these weights results in the weighted jackknife estimate being equal to the original estimate. If the pairs (xit Yt) satisfy the following model, Y^Rx^e^ where the et are independent with distribution N{Q, where fit is the estimate of R from the i'A sample, and R is the true population ratio. The percentage bias of method t is calculated as where vti is the variance estimate based on method t and calculated from the ith simulated sample. 75 Similarly, the mean squared error of method t as an estimator of the standard error of ft is calculated as: MSE. = 1 V; (jvT-^MSE(ft) ) ' 5000 ti \" \" ' Finally, the relative mean squared error of method t is obtained by calculating the percentage difference between its mean squared error and the minimum mean squared error among all methods for a given sample size; that is, RMSE,= MSE,-m™[MSEJ min [MSEJ •100 The results obtained for all seven methods of variance estimation and all sample sizes for a given real or artificial population, will be presented in a single table. From these results, we will attempt to draw general conclusions regarding the relative performance of these methods of estimating the variance of ft in the context of populations similar to those faced by the Ministry of Forests. 5.4 Results from Real Populations We will first discuss the results from each population and will thereafter summarize the overall results from the three Ministry data sets. The results for population A, which was approximately heteroscedastic with zero intercept, summarized in Table 1, indicate that Cochran I and Fuller's method are the best estimators regardless of the sample size. For the smaller sample sizes (n=5, 10, 20) these two methods out-perform the other methods by approximately 5%. For the larger sample sizes, (n=30, 50, 100) 76 Cochran I and Fuller's method significantly out-perform the remaining methods, with a mean squared error approximately 10% lower than for the other methods. The bias of the bootstrap methods is significantly higher than that of the other methods; but all methods underestimate the true standard error for the smaller sample sizes. However, at the larger sample sizes, all methods are approximately unbiased. As the sample size increases, the bias of all methods is steadily reduced from -5% at n=5 to -0.5% at n=50. Peculiarly, for n=100 the bias increases to -1.3%. As for the weighted versions of the resampling methods, both the weighted jackknife and the weighted bootstrap have mean squared errors similar to mean squared errors of their unweighted counterparts. The bootstrap methods, though approximately unbiased, have much larger mean squared errors than the other methods, and their relative mean squared error rises with increasing sample sizes. This may be due to the need for a finite population modification to the bootstrap. The results for population B, which was close to ideal, tabulated in Table 2, can be summarized as follows: All methods, except the two bootstrap-based methods at larger sample sizes, perform similarly. Once again, Cochran I and Fuller's method have the lowest mean squared error. However, the remaining methods do not show significantly larger mean squared errors. The average difference in the squared errors between the methods is approximately 2%. Once again, the bootstraps do not perform very well at the largest sample size, though they do perform well at sample sizes less than 50. In both this and the previous population, the bias of the bootstrap methods does not differ greatly from the other methods, however the relative mean squared error does differ significantly. The weighted resampling methods perform similar to their unweighed counterparts. Once again, the bias of all methods is steadily reduced as the sample size increases. However at n=50, the bias increases and becomes positive, before dropping to almost zero at n=100. 77 The results for population C, which exhibited a non-zero intercept and multi-modal distribution of weights, summarized in Table 3, are very similar to the results from population B. Once again, all methods perform similarly, both in terms of mean squared error and in terms of bias. However, for this population, the bootstraps do not show as high a relative squared error at the largest sample size as for population B. Once again, the bias has peculiar behaviour as the sample size increases. The combined results from all three populations are as follows. For the larger sample sizes, all methods except for the bootstrap perform similarly. All methods have approximately the same mean squared error and are approximately unbiased, except for the bootstrap which has longer relative mean squared errors. For the smaller sample sizes the methods differ and, there is sometimes, significant underestimation of the standard error of R. The weighted jackknife has a mean squared error which is slightly smaller than the mean squared error of the unweighed jackknife. The Cochran II method, which is also Hinkley's weighted jackknife, has a mean squared error and bias which is quite similar to that of the two jackknife-based methods. However, the Cochran II method has a consistently lower mean squared error than the unweighted bootstrap. In summary, it is the particular characteristics of the population that determine the quality of performance of a particular method for that population. Based on the above, the following conclusions may be drawn. For populations with zero intercept and slight heteroscedasticity, Cochran I and Fuller's method perform best, regardless of sample size. For populations with non-zero intercept and weights distributed with multiple modes, all seven methods perform similarly. Thus, one may conclude that Cochran I and Fuller's method are the most robust with respect to these three populations. However, it is difficult to generalize these conclusions; it is still not known which estimates are robust with respect to the intercept, which are robust with respect to the variance structure and which are robust with respect to both. This 78 is because we have not isolated these population characteristics and studied their effects in a more controlled setting. Therefore, to study the sensitivity of the seven estimators, a similar simulation was performed on artificial data. However, these simulations will not involve sample sizes with large sampling fractions because we have already established that at these large sampling fractions the bootstrap methods have higher mean squared error. 5.5 Results from Artificial Populations The purpose of conducting empirical studies on artificial populations is to study the effects of non-zero intercept and heteroscedasticity on the relative performance of the various estimators. The six artificial populations described in Section 5.3 were created with this in mind. The six populations can, in a very general sense, be described as the results of a 3 by 2 factorial design; the factors being, respectively, heteroscedasticity and non-zero intercept. The results of the empirical study on these artificial populations are summarized in Tables 4-9. For each of the six artificial populations, all seven methods were tested at sample sizes 10, 20 and 50. Because of the consistency of results across sample sizes, sample sizes 5, 30 and 100 were not used. Rather than present the results on a population by population basis, the results will be presented with respect to the two factors. The sensitivity of the estimators with respect to the intercept will be discussed first, followed by sensitivity with respect to the variance structure and finally, with respect to both factors. All results are consistent across the sample sizes. Thus, if one method performs well at the smallest sample size, it will also perform well at larger sample sizes. Consequently, except for the following two observations, no further mention of change over sample sizes will be made. As sample sizes get larger, the discrepancies between the different methods get smaller, as one would expect. The second observation is that for the smaller sample sizes, the bias is consistently 79 negative; however, the bias reduces at the larger sample sizes resulting in approximately unbiased estimators. For the ideal population Y00 (homoscedastic and zero intercept), Cochran I and Fuller's method are slightly better than the other methods in terms of mean squared error, the difference being approximately 2%. However, as we move towards homoscedastic population with a non-zero intercept (from Y00, Table 4 to Y01, Table 7), it is clear that Cochran I and Fuller's method have a substantially smaller mean squared error than the other methods, though all methods are approximately unbiased. Similarly, for populations with heteroscedastic variance structure, the presence of a non-zero intercept (from Y10, Table 5 to Yl l , Table 8 and from Y20, Table 6 to Y21, Table 7) causes Cochran I and Fuller's method to either have smaller mean squared errors than the other methods or to reduce the difference in the mean squared errors between these two groups. Thus, it may be concluded that Cochran I and Fuller's method are more robust with respect to non-zero intercept than the other methods. In terms of bias, there are minor differences between the two groups in the presence of a non-zero intercept. In the presence of heteroscedasticity, Cochran II and the resampling-based estimators have smaller mean squared errors than Cochran I and Fuller's method. For populations with zero intercept, the presence of heteroscedasticity causes an increase in the relative mean squared error of Cochran I and Fuller's estimator, though the bias remains unaffected. For populations with non-zero intercept, the presence of heteroscedasticity once again causes the relative mean squared errors of Cochran I and Fuller's method to increase. However, because these two methods were substantially better than the other methods for homoscedastic and non-zero intercept populations (Y01), it is not until substantial heteroscedasticity is present (Y21) that Cochran I and Fuller's method exhibit larger mean squared error. Based on the preceding results, we conclude that Cochran II and the resampling methods are more robust with respect to heteroscedasticity than 80 the other two methods. It is interesting to note that as we increase heteroscedasticity in the presence of non-zero intercept (from Y01, Table 7 to Y l l , Table 8 to Y21, Table 9), resampling-based methods along with Cochran I, improve relative to the analytic methods. The relative improvement is possibly due to the analytic methods performing worse than the resampling-based methods in the presence of both heteroscedasticity and non-zero intercept. In terms of both a non-zero intercept and non-constant variance, no estimator performs consistently well; that is, no estimator is robust with respect to both non-zero intercept and non-constant variance. In the factorial design analogy, there are no interaction effects. That is, some estimators are robust with respect to heteroscedasticity whereas others are robust with respect to non-zero intercept. However, no estimators are robust with respect to the presence of both factors. The performance of the resampling estimators was very similar in terms of the mean squared errors; for all population sizes, the mean squared errors of the four estimators rarely differed by more than 2%. However, the bootstrap-based estimators were more biased than the jackknife-based estimators at the smaller sample sizes i.e. n = 10, 20. On average, the underestimation of the standard error of ft by the bootstrap-based estimators was approximately 5% less than the underestimation by the jackknife-based estimators. In terms of the weighted and unweighted versions, the weighted jackknife always outperformed the unweighted jackknife, though the average difference in mean squared errors was approximately 0.5%. For non-ideal populations, the bias of the weighted jackknife slightly exceeded the bias of the unweighted jackknife. The weighted and unweighted bootstraps showed different robustness; the weighted bootstrap was more robust with respect to heteroscedasticity, whereas the unweighted bootstrap was more robust with respect to non-zero intercept. For both the jackknife and the bootstrap, the differences between the weighted and unweighted versions were minimal. The mean squared errors rarely differed by more than 1%. 81 It is clear from the table that for a homoscedastic population, regardless of the intercept, Cochran I and Fuller's method perform better than the remaining five methods. In fact, when the intercept is non-zero and the error structure is homoscedastic, Cochran I and Fuller's method produce mean squared errors approximately 10% lower than those of the other methods. Of the five remaining methods, the performance is very similar. On the other hand, for very heteroscedastic populations, that is Y20 and Y21, Cochran II and the resampling methods out-perform Cochran I and Fuller's method. However, in these instances the discrepancy between the two groups is half of what it was for the homoscedastic case; that is 5%. For the populations Y10 and Yl l , the results are quite interesting: The resampling methods, along with the Cochran II method, out-perform (by approximately 2%) the remaining two methods for small sample sizes when the intercept is zero. However when the intercept is non-zero, the two methods (Cochran I and Fuller's) out-perform the remaining methods by approximately 9%. 5.6 Summary The overall conclusions of the study on artificial and real populations are that for a homoscedastic population, with zero intercept, one should use either Cochran I or Fuller's method; since Cochran I is much simpler, it should be used instead of Fuller's method. If the population is highly heteroscedastic, then the resampling-based methods or the Cochran II method should be used. The presence of a non-zero intercept should be considered together with the variance structure. If the violation of the homoscedastic variance assumption exceeds, in a qualitative sense, the violation of the zero intercept assumption, then the resampling based methods should be preferred over the remaining two methods. Otherwise, Cochran I or Fuller's method should be used. 82 In terms of sensitivity to population characteristics, Cochran I and Fuller's method are robust with respect to a non-zero intercept, whereas the other five estimators are more robust with respect to heteroscedasticity. As to the robustness with respect to both characteristics, no estimators performed consistently well. However, due to the higher spread between the two groups for homoscedastic and non-zero intercept populations than for heteroscedastic and zero intercept populations, Cochran I and Fuller's method could be said to be more robust. As for the smaller sample sizes, all estimators underestimate the true mean squared error, with average underestimation of approximately 4% at sample size 10 and approximately 2% at sample size 20. The jackknife-based methods had a consistently lower bias than the other methods, whereas the bootstrap-based methods had a consistently higher bias. The resampling-based estimators performed very similarly - especially if we consider the Cochran II method as a weighted jackknife method. This seems reasonable not only because the Cochran II estimator can be derived from the influence function-based approach to weighting the jackknife estimator, but also because its performance in the empirical study was very similar to the resampling-based methods. Thus, it is apparent that the weighted versions of the jackknife and bootstrap do not perform significantly better than their unweighted counterparts within the context of estimation of the variance of the classical ratio estimator, and within the context of the Ministry of Forests' data. 83 Chapter 6 Conclusions and Recommendations In this chapter, specific conclusions concerning weigh scaling at the Ministry of Forests will be made. In particular, the issues of variance estimation and modification of resampling-based estimators will be addressed. Recommendations for the choice of robust variance estimators will also be made. The first, and possibly most significant, conclusion is that the current variance estimator, Cochran II, is effective at sample sizes well below the present minimum requirement of 30. Furthermore, empirical studies show that the seven methods studied all performed consistently well in terms of squared error at sample sizes as low as 10. However at smaller sample sizes, the estimators also underestimated the true standard error of Y, the average percentage bias being approximately -4%. This implies that confidence intervals constructed at these small sample sizes are 5% shorter on average than they would be if the estimators were unbiased. Therefore, a 1% confidence interval estimated using 10 samples will likely have a lower than 1% probability of including the population total. The bias reduces to -2% at sample size 20, and the estimators are effectively unbiased at larger sample sizes. Thus, the issue of bias only arises at smaller sample sizes. The second conclusion is that all seven methods have approximately similar mean squared errors. At the smaller sample sizes, the difference between the estimators range from 5-10%, the exact differences being dependent upon population characteristics. Cochran I and Fuller's method were found to be bias-robust, whereas Cochran II and the resampling-based estimators were found to be heteroscedastic-robust. No estimators were robust with respect to both bias and heteroscedasticity. 84 To determine the best estimators for use in weigh scaling, the Ministry would have to decide whether non-zero intercepts or heteroscedasticity is the major \"violation\" in their data, and then select an appropriately robust estimator. The third and final conclusion is that in terms of squared error and bias, the unweighted resampling-based variance estimators performed almost as well as their weighted counterparts. Perhaps in different estimation problems, the weighted resampling-based methods would perform differently from their unweighted counterparts. However, for the variance of the classical ratio estimator, the two versions have approximately similar mean squared errors. There are three major areas that require further research. The first is the empirical performance of the estimators at small sample sizes for non-normally distributed and finite populations. Recall that in the empirical study discussed in Chapter 5, the errors and the weights were simulated using the normal distribution. It is not clear whether our conclusions, particularly about the small sample effectiveness of the estimators, can be extended to non-normal populations. Furthermore, the finite population adjustment needs to be studied, particularly for samples with large sampling fractions. These issues were not dealt with in this thesis because the Ministry populations are approximately normal and because their samples are drawn with small sampling fractions. A second area which requires further study is the combination of variance and ratio estimators. Recall that in Chapter 2 we choose to restrict our attention to the classical ratio estimator, and thus these conclusions only apply to estimation of population total by the classical ratio estimator. Perhaps there exists a combination of ratio and variance estimators which is in some sense optimal for the estimation of confidence intervals in the context of the Ministry of Forests' applications. Finally, a third area which requires further research is resampling-based confidence intervals, particularly bootstrap-based confidence intervals. These appear to be particularly promising because of their non-parametric and non-symmetric approaches. 85 The specific recommendations arising from this research are as follows: The Ministry of Forests should select either Cochran I or Cochran II as the variance estimator of their estimates of population total volume. The particular choice of estimator should be based on the types of populations encountered by the Ministry. 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Bias and Confidence in not Quite Large Samples, Abstract. Annals of Mathematical Statistics, 29, 614. 65. Wu, C.F.J. (1982). Estimation of Variance of the Ratio Estimator. Biometrika, 69, 183-189. 66. Wu, C.F.J. (1986). Jackknife, Bootstrap and Other Resampling Methods in Regression Analysis. The Annals of Statistics, 14, 1261-1295. 67. Wu, C.F.J. and Deng, L.Y. (1983). Estimation of Variance of the Ratio Estimator: An Empirical Study. Scientific Influence, Data Analysis and Robustness. Academic Press, Inc., New York, New York. 90 Table 1 Population A Empirical Results of Variance Estimators RMSE=Relative MSE PB=Percentage Bias Sample Size 5 10 20 30 50 100 Method RMSE PB RMSE PB RMSE PB RMSE PB RMSE PB RMSE PB Cochran I 0.0 -4.7 0.0 -2.8 0.2 -2.7 0.2 -0.5 0.6 -0.2 1.8 -1.3 Cochran II 5.1 -4.2 5.7 -2.6 7.0 -2.6 8.0 -0.5 7.8 -0.2 9.0 -1.3 Fuller 0.1 -4.6 0.0 -2.7 0.0 -2.6 0.0 -0.5 0.0 -0.2 0.0 -1.3 Jackknife 5.4 -4.3 6.1 -2.7 7.3 -2.7 8.2 -0.5 8.0 -0.2 9.3 -1.3 Wtd. Jackknife 5.3 -4.3 5.9 -2.6 7.2 -2.7 8.1 -0.5 7.9 -0.2 9.2 -1.3 Bootstrap I 3.7 -13.8 7.6 -7.3 12.2 -4.9 10.1 -2.0 11.6 -1.1 26.0 -1.8 Wtd. Bootstrap 3.7 -14.1 7.8 -7.4 12.6 -5.0 10.2 -2.1 11.7 -1.2 26.3 -1.8 Results are summarized from 5,000 simple random samples. Bootstrap estimates involved 2,500 resamples. 91 Table 2 Population B Empirical Results of Variance Estimators RMSE=Relative MSE Sample Size PB=Percentage Bias 5 10 20 30 50 100 Method RMSE PB RMSE PB RMSE PB RMSE PB RMSE PB RMSE PB Cochran 1 0.0 -7.4 0.0 -3.7 0.0 -1.6 0.4 -0.3 0.7 1.2 0.6 -0.5 Cochran II 0.9 -7.0 1.9 -3.4 1.6 -1.4 0.5 -0.2 1.3 1.2 1.4 -0.5 Fuller 0.0 -7.3 0.0 -3.7 0.0 -1.5 0.0 -0.3 0.0 1.2 0.0 -0.5 Jackknife 2.9 -6.2 2.6 -2.9 1.8 -1.2 0.7 -0.1 1.7 1.3 1.3 -0.5 Wtd. Jackknife 1.8 -6.6 2.2 -3.2 1.7 -1.3 0.6 -0.2 1.5 1.2 1.4 -0.5 Bootstrap 1 0.8 -16.0 3.6 -8.1 3.2 -3.8 1.1 -1.8 0.5 0.2 11.1 -1.0 Wtd. Bootstrap 1.2 -16.5 3.7 -8.3 3.3 -3.9 1.1 -1.9 0.3 0.2 11.2 -1.0 Results are summarized from 5,000 simple random samples. Bootstrap estimates involved 2,500 resamples. 92 T a b l e 3 Population C Empirical Results of Variance Estimators RMSE=Relative MSE Sample Size PB=Percentage Bias 5 10 20 30 50 100 Method RMSE PB RMSE PB RMSE PB RMSE PB RMSE PB RMSE PB Cochran 1 4.2 -3.4 0.1 -2.5 0.0 -2.1 0.0 -1.6 0.0 0.2 0.0 1.3 Cochran II 6.1 -3.2 1.1 -2.5 1.9 -2.1 1.4 -1.5 1.0 0.2 1.5 1.3 Fuller 4.1 -3.4 0.0 -2.5 0.2 -2.1 0.1 -1.6 0.3 0.2 1.5 1.3 Jackknife 6.9 -3.1 1.5 -2.4 2.2 -2.1 1.6 -1.5 1.1 0.2 1.6 1.3 Wtd. Jackknife 6.5 -3.1 1.2 -2.5 2.0 -2.1 1.5 -1.5 1.1 0.2 1.5 1.3 Bootstrap 1 0.0 -13.1 1.4 -7.3 4.9 -4.5 5.0 -3.2 1.9 -0.7 3.1 0.8 Wtd. Bootstrap 0.1 -13.3 1.6 -7.4 5.1 -4.5 5.2 -3.2 1.9 -0.8 3.1 0.8 Results are summarized from 5,000 simple random samples. Bootstrap estimates involved 2,500 resamples. 93 Population Y00 Empirical Results of Variance Estimators RMSE=Relative MSE PB=Percentage Bias Sample Size 10 20 50 Method RMSE PB RMSE PB RMSE PB Cochran 1 0.0 -3.7 0.0 -2.2 0.0 0.0 Cochran II 1.1 -3.5 2.2 -2.1 2.9 0.0 Fuller 0.0 -3.7 0.0 -2.2 0.0 0.0 Jackknife 1.6 -3.4 2.5 -2.1 3.1 0.0 Wtd. Jackknife 1.3 -3.5 2.3 -2.1 3.0 0.0 Bootstrap 1 1.9 -8.2 4.0 -4.5 4.0 -0.8 Wtd. Bootstrap 2.3 -8.4 4.3 -4.6 4.0 -0.9 Results are summarized from 5,000 simple random samples. Bootstrap estimates involved 2,500 resamples. 94 Table 5 Population Y10 Empirical Results of Variance Estimators RMSE=Relative MSE PB=Percenlage Bias Sample Size 10 20 50 Method RMSE PB RMSE PB RMSE PB Cochran I 2.2 -4.0 1.2 -2.2 0.5 0.0 Cochran II 0.0 -3.8 0.0 -2.1 0.0 0.0 Fuller 2.2 -4.0 1.1 -2.2 0.4 0.0 Jackknife 1.0 -3.6 0.5 -2.0 0.3 0.1 Wtd. Jackknife 0.5 -3.7 0.2 -2.1 0.1 0.0 Bootstrap I 1.0 -8.7 1.5 -4.6 0.6 -0.9 Wtd. Bootstrap 1.5 -8.8 1.8 -4.6 0.8 -0.9 Results are summarized from 5,000 simple random samples. Bootstrap estimates involved 2,500 resamples. 95 Population Y20 Empirical Results of Variance Estimators RMSE=Relative MSE PB=Percentage Bias Sample Size 10 20 50 Method RMSE PB RMSE PB RMSE PB Cochran 1 5.2 -4.3 4.2 -2.2 3.6 0.0 Cochran II 0.0 -4.3 0.0 -2.2 0.0 0.0 Fuller 5.1 -4.3 4.1 -2.2 3.2 0.0 Jackknife 1.4 -3.9 0.8 -2.0 0.5 0.1 Wtd. Jackknife 0.7 -4.1 0.4 -2.1 0.2 0.0 Bootstrap 1 1.1 -9.2 1.1 -4.7 0.3 -1.0 Wtd. Bootstrap 1.6 -9.2 1.4 -4.7 0.4 -0.9 Results are summarized from 5,000 simple random samples. Bootstrap estimates involved 2,500 resamples. 96 Population Y01 Empirical Results of Variance Estimators RMSE=Relative MSE Sample Size PB=Percentage Bias 10 20 50 Method RMSE PB RMSE PB RMSE PB Cochran I 0.0 -2.1 0.0 -0.8 0.0 1.7 Cochran II 11.6 -1.7 11.2 -0.6 11.5 1.8 Fuller 0.0 -2.1 0.0 -0.8 0.1 1.7 Jackknife 12.9 -1.5 11.5 -0.5 11.6 1.8 Wtd. Jackknife 12.2 -1.6 11.3 -0.6 11.5 1.8 Bootstrap I 11.4 -6.0 11.3 -2.8 9.7 0.9 Wtd. Bootstrap 10.2 -6.4 10.7 -2.9 9.0 0.8 Results are summarized from 5,000 simple random samples. Bootstrap estimates involved 2,500 resamples. 97 Population Y11 Empirical Results of Variance Estimators RMSE=Relative MSE PB=Percentage Bias Sample Size 10 20 50 Method RMSE PB RMSE PB RMSE PB Cochran I 0.0 -2.3 0.0 -1.4 0.0 1.7 Cochran II 9.1 -1.9 8.9 -1.2 9.2 1.8 Fuller 0.0 -2.2 0.0 -1.3 0.1 1.7 Jackknife 10.9 -1.7 9.6 -1.1 9.6 1.8 Wtd. Jackknife 10.0 -1.8 9.2 -1.2 9.4 1.8 Bootstrap I 9.2 -6.3 9.7 -3.4 6.9 0.9 Wtd. Bootstrap 8.6 -6.6 9.4 -3.6 6.5 0.8 Results are summarized from 5,000 simple random samples. Bootstrap estimates involved 2,500 resamples. 98 Population Y21 Empirical Results of Variance Estimators RMSE=Relative MSE PB=Percentage Bias Sample Size 10 20 50 Method RMSE PB RMSE PB RMSE PB Cochran I 3.9 -4.0 3.0 -2.4 3.0 0.4 Cochran II 0.0 -3.9 0.0 -2.4 0.4 0.5 Fuller 3.8 -4.0 2.9 -2.4 2.7 0.4 Jackknife 1.7 -3.5 0.9 -2.2 1.0 0.6 Wtd. Jackknife 0.8 -3.7 0.4 -2.3 0.7 0.5 Bootstrap I 0.9 -8.8 1.5 -4.9 0.0 -0.5 Wtd. Bootstrap 1.4 -8.8 1.8 -4.9 0.1 -0.5 Results are summarized from 5,000 simple random samples. Bootstrap estimates involved 2,500 resamples. 99 TOT Volume (Cubic Metres) Volume (Cubic Metres) Volume (Cubic Metres) 20 40 60 80 120 20 40 60 80 120 20 40 60 80 120 Population A Population B Weight of Truckloads (Tonnes) The Above Line is ( Population Ratio * Weight) Population C "@en ; edm:hasType "Thesis/Dissertation"@en ; edm:isShownAt "10.14288/1.0098348"@en ; dcterms:language "eng"@en ; ns0:degreeDiscipline "Statistics"@en ; edm:provider "Vancouver : University of British Columbia Library"@en ; dcterms:publisher "University of British Columbia"@en ; dcterms:rights "For non-commercial purposes only, such as research, private study and education. Additional conditions apply, see Terms of Use https://open.library.ubc.ca/terms_of_use."@en ; ns0:scholarLevel "Graduate"@en ; dcterms:title "Resampling-based variance estimators in ratio estimation with application to weigh scaling"@en ; dcterms:type "Text"@en ; ns0:identifierURI "http://hdl.handle.net/2429/29195"@en .