Electronic Theses and Dissertations

Identifier

6533

Date

2020

Document Type

Thesis

Degree Name

Master of Science

Major

Public Health

Concentration

Biostatistics

Committee Member

Hongmei Zhang

Committee Member

Yu Joyce Jiang

Committee Member

Dale Bowman

Abstract

The selection of variables in regression problems has occupied the minds of many statisticians. Several Bayesian variable selection methods have been developed, and we concentrate on the following method.This thesis which is a continuation of a project by Zhang textit{et al.} cite{Zha} published in Bayesian Analysis in 2016, first describes the general idea behind Bayes Inference, various sampling methods based on Bayes theorem.Then we present a Bayesian variable selection method based on an extension of the Zellner’s g-prior in linear models. More specifically, we propose a two-component G-prior, wherein a tuning parameter, calibrated by use of pseudo variables, is introduced to adjust the distance between the two components. We Assess the impact of tuning parameter b, the distance between important and unimportant variables, on the selection of variables by controlling Bayesian false model selection rate with respect to unimportant variables based on creating pseudo variables. We show that implementing the proposed prior in variable selection is more efficient than using the Zellner’s g-prior.

Comments

Data is provided by the student.

Library Comment

Dissertation or thesis originally submitted to the local University of Memphis Electronic Theses & dissertation (ETD) Repository.

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