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Dirichlet Process Mixture Models with Shrinkage Prior
thesis
posted on 2021-08-01, 00:00 authored by Dawei DingWe 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, GeorgeChair
Wang, JingKarabatsos, GeorgeDepartment
Mathematics, Statistics, and Computer ScienceDegree Grantor
University of Illinois at ChicagoDegree Level
- Doctoral
Degree name
PhD, Doctor of PhilosophyCommittee Member
Ouyang, Cheng Tulabandhula, Theja Yang, JieSubmitted date
August 2021Thesis type
application/pdfLanguage
- en