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Bayesian Semiparametric Measurement ...

Liu, Chang.

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

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Bayesian Semiparametric Measurement Error Models: Estimation, Variable Selection and Fast Algorithms./
作者:	Liu, Chang.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2017,
面頁冊數:	251 p.
附註:	Source: Dissertation Abstracts International, Volume: 78-10(E), Section: B.
Contained By:	Dissertation Abstracts International78-10B(E).
標題:	Statistics. -
電子資源:	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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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.
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