- Library Home /
- Search Collections /
- Open Collections /
- Browse Collections /
- UBC Theses and Dissertations /
- Maximum weighted likelihood estimation
Open Collections
UBC Theses and Dissertations
UBC Theses and Dissertations
Maximum weighted likelihood estimation Wang, Steven Xiaogang
Abstract
A maximum weighted likelihood method is proposed to combine all the relevant data from different sources to improve the quality of statistical inference especially when the sample sizes are moderate or small. The linear weighted likelihood estimator (WLE), is studied in depth. The weak consistency, strong consistency and the asymptotic normality of the WLE are proved. The asymptotic properties of the WLE using adaptive weights are also established. A procedure for adaptively choosing the weights by using cross-validation is proposed in the thesis. The analytical forms of the "adaptive weights" are derived when the WLE is a linear combination of the MLE's. The weak consistency and asymptotic normality of the WLE with weights chosen by cross-validation criterion are established. The connection between WLE and theoretical information theory is discovered. The derivation of the weighted likelihood by using the maximum entropy principle is presented. The approximations of the distributions of the WLE by using saddlepoint approximation for small sample sizes are derived. The results of the application to the disease mapping are shown in the last chapter of this thesis.
Item Metadata
Title |
Maximum weighted likelihood estimation
|
Creator | |
Publisher |
University of British Columbia
|
Date Issued |
2001
|
Description |
A maximum weighted likelihood method is proposed to combine all the relevant
data from different sources to improve the quality of statistical inference especially
when the sample sizes are moderate or small.
The linear weighted likelihood estimator (WLE), is studied in depth. The weak
consistency, strong consistency and the asymptotic normality of the WLE are proved.
The asymptotic properties of the WLE using adaptive weights are also established.
A procedure for adaptively choosing the weights by using cross-validation is proposed
in the thesis. The analytical forms of the "adaptive weights" are derived when the
WLE is a linear combination of the MLE's. The weak consistency and asymptotic normality
of the WLE with weights chosen by cross-validation criterion are established.
The connection between WLE and theoretical information theory is discovered. The
derivation of the weighted likelihood by using the maximum entropy principle is presented.
The approximations of the distributions of the WLE by using saddlepoint
approximation for small sample sizes are derived. The results of the application to
the disease mapping are shown in the last chapter of this thesis.
|
Extent |
5580559 bytes
|
Genre | |
Type | |
File Format |
application/pdf
|
Language |
eng
|
Date Available |
2009-10-09
|
Provider |
Vancouver : University of British Columbia Library
|
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.
|
DOI |
10.14288/1.0090880
|
URI | |
Degree | |
Program | |
Affiliation | |
Degree Grantor |
University of British Columbia
|
Graduation Date |
2001-11
|
Campus | |
Scholarly Level |
Graduate
|
Aggregated Source Repository |
DSpace
|
Item Media
Item Citations and Data
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.