The model based approach to smoothing
department or school: Science, Faculty of
department or school: Statistics, Department of
data provider: DSpace
degree campus: UBCV
creator: Mazzi, Sonia V.T.
issued: 2009-03-31T20:36:17Z
issued: 1997
related degree: Doctor of Philosophy - PhD
degree grantor: University of British Columbia
description: Nonparametric regression methods are devised in order to obtain a smooth fit of a regression curve without having to specify a parametric model. This, in principle, provides a flexible approach to curve estimation. It is shown how splines are not such a flexible tool for smoothing. It is proposed to use the modern approach of state space modeling instead, to solve a variety of smoothing problems. This model based approach alleviates estimation and computational problems as well as provides insightful solutions to smoothing problems.
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/6666
extent: 5017486 bytes
format: application/pdf
note: T H E M O D E L B A S E D A P P R O A C H T O S M O O T H I N G by SONIA V . T . M A Z Z I L i e , Universidad Nacional de Cordoba, Argentina, 1989 M . S c , The University of British Columbia, 1992 A T H E S I S S U B M I T T E D IN P A R T I A L F U L F I L L M E N T O F T H E R E Q U I R E M E N T S F O R T H E D E G R E E O F D O C T O R O F P H I L O S O P H Y in T H E F A C U L T Y O F G R A D U A T E S T U D I E S D E P A R T M E N T O F S T A T I S T I C S We accept this thesis as conforming to the required standard T H E U N I V E R S I T Y O F B R I T I S H C O L U M B I A January 1997 © S o n i a V . T . Mazzi , 1997 In presenting this thesis in partial fulfilment of the requirements for an advanced degree at the University of British Columbia, I 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 of €Tr\ Tl'STt'CS The University of British Columbia Vancouver, Canada Date Sartygr^j 39 -No, DE-6 (2/88) The model based approach to smoothing Abstract Nonparametric regression methods are devised in order to obtain a smooth fit of a regression curve without having to specify a parametric model. This, in principle, provides a flexible approach to curve estimation. It is shown how splines are not such a flexible tool for smoothing. It is proposed to use the modern approach of state space modeling instead, to solve a variety of smoothing problems. This model based approach alleviates estimation and computational problems as well as provides insightful solutions to smoothing problems. n Contents Abstract i i Table of Contents i i i List of Tables v i i i List of Figures ix Acknowledgements x 1 Introduction 1 1.1 Motivation 1 1.2 Co…
has type: Thesis/Dissertation
date issued: 1997-05
is shown at: 10.14288/1.0087989
language: eng
degree discipline: Statistics
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
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.
scholar level: Graduate
type: Text
identifier URI: http://hdl.handle.net/2429/6666