The Semi-Hierarchical Dirichlet Process and Its Application to Clustering Homogeneous Distributions
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Date
2021
Journal Title
Journal ISSN
Volume Title
Publisher
INT SOC BAYESIAN ANALYSIS
Abstract
Assessing homogeneity of distributions is an old problem that has received considerable attention, especially in the nonparametric Bayesian literature. To this effect, we propose the semi-hierarchical Dirichlet process, a novel hierarchical prior that extends the hierarchical Dirichlet process of Teh et al. (2006) and that avoids the degeneracy issues of nested processes recently described by Camerlenghi et al. (2019a). We go beyond the simple yes/no answer to the homogeneity question and embed the proposed prior in a random partition model; this procedure allows us to give a more comprehensive response to the above question and in fact find groups of populations that are internally homogeneous when I >= 2 such populations are considered. We study theoretical properties of the semi hierarchical Dirichlet process and of the Bayes factor for the homogeneity test when I = 2. Extensive simulation studies and applications to educational data are also discussed.
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Keywords
Bayes factors, Bayesian nonparametrics, partial exchangeability, posterior consistency, homogeneity test, BAYESIAN MODEL, INFERENCE, SELECTION, PRIORS