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Bayesian Semiparametric Measurement ...

Liu, Chang.

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Bayesian Semiparametric Measurement Error Models: Estimation, Variable Selection and Fast Algorithms.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Bayesian Semiparametric Measurement Error Models: Estimation, Variable Selection and Fast Algorithms./
Author:	Liu, Chang.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2017,
Description:	251 p.
Notes:	Source: Dissertation Abstracts International, Volume: 78-10(E), Section: B.
Contained By:	Dissertation Abstracts International78-10B(E).
Subject:	Statistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10268909
ISBN:	9781369823011

Bayesian Semiparametric Measurement Error Models: Estimation, Variable Selection and Fast Algorithms.
Liu, Chang.

Bayesian Semiparametric Measurement Error Models: Estimation, Variable Selection and Fast Algorithms. - Ann Arbor : ProQuest Dissertations & Theses, 2017 - 251 p.

Source: Dissertation Abstracts International, Volume: 78-10(E), Section: B.

Thesis (Ph.D.)--University of Rochester, 2017.

This thesis presents Bayesian estimation, variable selection and fast algorithms for semiparametric measurement error models. We will extend the existing Bayesian estimation procedures for measurement error models to allow for heteroscedasctic regression errors from a normal distribution and further to relax the normality assumptio. Bayesian penalized splines will be used to estimate the variance functions and Dirichlet process mixtures models will be applied for non-normal regression errors. Variable selection procedures for this class of model will also be considered. We will apply Bayesian adaptive LASSO and group LASSO to jointly select and estimate the parametric and nonparametric components in a semiparametric model in the presence of measurement error, either in the linear or nonlinear covariate. Some extensions will be discussed to account for heteroscedasticity. Several computing strategies will be proposed to deal with high-dimensional data. To address the computational issues associated with Bayesian posterior inference, we will propose several mean field variational Bayes approximation algorithms for estimation and variable selection algorithms. Simulation studies will be conducted to evaluate each algorithm. The proposed algorithms will be applied to data on phthalate exposure, diet and semen quality.

ISBN: 9781369823011Subjects--Topical Terms:

517247
Statistics.

Bayesian Semiparametric Measurement Error Models: Estimation, Variable Selection and Fast Algorithms.
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020 $a 9781369823011
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100 1 $a Liu, Chang. $3 700219
245 1 0 $a Bayesian Semiparametric Measurement Error Models: Estimation, Variable Selection and Fast Algorithms.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2017
300 $a 251 p.
500 $a Source: Dissertation Abstracts International, Volume: 78-10(E), Section: B.
500 $a Adviser: Sally W. Thurston.
502 $a Thesis (Ph.D.)--University of Rochester, 2017.
520 $a This thesis presents Bayesian estimation, variable selection and fast algorithms for semiparametric measurement error models. We will extend the existing Bayesian estimation procedures for measurement error models to allow for heteroscedasctic regression errors from a normal distribution and further to relax the normality assumptio. Bayesian penalized splines will be used to estimate the variance functions and Dirichlet process mixtures models will be applied for non-normal regression errors. Variable selection procedures for this class of model will also be considered. We will apply Bayesian adaptive LASSO and group LASSO to jointly select and estimate the parametric and nonparametric components in a semiparametric model in the presence of measurement error, either in the linear or nonlinear covariate. Some extensions will be discussed to account for heteroscedasticity. Several computing strategies will be proposed to deal with high-dimensional data. To address the computational issues associated with Bayesian posterior inference, we will propose several mean field variational Bayes approximation algorithms for estimation and variable selection algorithms. Simulation studies will be conducted to evaluate each algorithm. The proposed algorithms will be applied to data on phthalate exposure, diet and semen quality.
590 $a School code: 0188.
650 4 $a Statistics. $3 517247
690 $a 0463
710 2 0 $a University of Rochester. $b Medicine and Dentistry. $3 3265809
773 0 $t Dissertation Abstracts International $g 78-10B(E).
790 $a 0188
791 $a Ph.D.
792 $a 2017
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10268909