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Dirichlet Process Mixture Models with Shrinkage Prior

thesis
posted on 01.08.2021, 00:00 by Dawei Ding
We propose Dirichlet process mixture (DPM) models for prediction and cluster-wise variable selection, based on three choices of shrinkage baseline prior distributions for the linear regression coefficients, namely, conditional Laplace prior, Horseshoe prior and Normal-Gamma prior. We show in a simulation study that each of the three proposed DPM models tends to outperform the standard DPM model based on the non-shrinkage normal prior, in terms of predictive, variable selection, and clustering accuracy. This is especially true for the Horseshoe model and when the number of covariates exceeds the within-cluster sample size. Real data sets are analyzed to illustrate the proposed modeling methodology, where all proposed DPM models again attained better predictive accuracy.

History

Advisor

Wang, JingKarabatsos, George

Chair

Wang, JingKarabatsos, George

Department

Mathematics, Statistics, and Computer Science

Degree Grantor

University of Illinois at Chicago

Degree Level

Doctoral

Degree name

PhD, Doctor of Philosophy

Committee Member

Ouyang, Cheng Tulabandhula, Theja Yang, Jie

Submitted date

August 2021

Thesis type

application/pdf

Language

en

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