JSM 2005 - Toronto

Abstract #303331

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Legend: = Applied Session, = Theme Session, = Presenter
Activity Number: 396
Type: Contributed
Date/Time: Wednesday, August 10, 2005 : 10:30 AM to 12:20 PM
Sponsor: Section on Bayesian Statistical Science
Abstract - #303331
Title: Discriminant Analysis in Longitudinal Data through a Bayesian Semiparametric Model
Author(s): Rolando De la Cruz-Mesia*+ and Fernando A. Quintana and Peter Müller
Companies: Pontificia Universidad Catolica de Chile and Pontificia Universidad Catolica de Chile and The University of Texas M. D. Anderson Cancer Center
Address: Departamento de Estadistica, Santiago, 6904411, Chile
Keywords: Dependent Dirichlet process ; Discriminant analysis ; Longitudinal data ; MCMC sampling ; Nonlinear hierarchical models ; Nonparametric modeling
Abstract:

We discuss Bayesian statistical methods for the classification of observations into two or more groups based on longitudinal observations. Inference is based on a semiparametric hierarchical model for each group. Specifically, we propose a species sampling model prior for the distribution of the random effects. The unknown random effects distributions are allowed to vary across groups, but are modeled dependent by using a design vector to select different features of a single underlying random probability measure. The resulting model can be interpreted as an extension of the dependent Dirichlet process (DDP) model. Relevant posterior distributions are summarized using Markov chain Monte Carlo methods. The method is illustrated with data from a study involving 173 pregnant women. The main objective in this study is to predict normal versus abnormal pregnancy outcomes from data available at early stages of pregnancy.

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