Bayesian hierarchically weighted finite mixture models for samples of distributions.

Biostatistics
Abel RodriguezJack Taylor

Abstract

Finite mixtures of Gaussian distributions are known to provide an accurate approximation to any unknown density. Motivated by DNA repair studies in which data are collected for samples of cells from different individuals, we propose a class of hierarchically weighted finite mixture models. The modeling framework incorporates a collection of k Gaussian basis distributions, with the individual-specific response densities expressed as mixtures of these bases. To allow heterogeneity among individuals and predictor effects, we model the mixture weights, while treating the basis distributions as unknown but common to all distributions. This results in a flexible hierarchical model for samples of distributions. We consider analysis of variance-type structures and a parsimonious latent factor representation, which leads to simplified inferences on non-Gaussian covariance structures. Methods for posterior computation are developed, and the model is used to select genetic predictors of baseline DNA damage, susceptibility to induced damage, and rate of repair.

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Citations

Apr 1, 2010·Journal of the American Statistical Association·Abel RodríguezAlan E Gelfand
Feb 7, 2014·Bioinformatics·David C KesslerDavid B Dunson
Oct 27, 2010·PLoS Computational Biology·Misha KoshelevP Read Montague
Nov 1, 2011·Food and Chemical Toxicology : an International Journal Published for the British Industrial Biological Research Association·Chris TheobaldGraham Horgan
May 9, 2012·Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences·Sandrine MeylanJean Clobert

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