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A0458
Title: Using variable selection within Bayesian clustering to explore dependence between categorical variables Authors:  Michail Papathomas - School of Mathematics and Statistics, University of St Andrews (United Kingdom) [presenting]
Sylvia Richardson - MRC Biostatistics - Cambridge (United Kingdom)
Abstract: Detecting interactions when analysing data sets created by large studies is becoming increasingly important in the Social sciences, Economics and Biostatistics. Investigating complex dependence structures within a linear modelling framework is not straightforward due to the difficulty in searching an unwieldy large space of competing models. One approach for reducing the dimensionality of the problem is to utilize a Bayesian modelling approach based on the Dirichlet process. We investigate the relation between the Dirichlet process and linear modelling, and discuss the utility of the Dirichlet process for the exploration of high order interactions, especially when sparse data are analysed.