title: Semiparametric Bayesian classification with longitudinal markers.
abstract: We analyse data from a study involving 173 pregnant women. The data are observed values of theβhuman chorionic gonadotropin hormone measured during the first 80 days of gestational age, including from one up to six longitudinal responses for each woman. The main objective in this study is to predict normalversusabnormal pregnancy outcomes from data that are available at the early stages of pregnancy. We achieve the desired classification with a semiparametric hierarchical model. Specifically, we consider a Dirichlet process mixture prior for the distribution of the random effects in each group. The unknown random-effects distributions are allowed to vary across groups but are made dependent by using a design vector to select different features of a single underlying random probability measure. The resulting model is an extension of the dependent Dirichlet process model, with an additional probability model for group classification. The model is shown to perform better than an alternative model which is based on independent Dirichlet processes for the groups. Relevant posterior distributions are summarized by using Markov chain Monte Carlo methods.
authors.affiliation: Pontificia Universidad Católica de Chile, Santiago, Chile.
authors.forename: Rolando
authors.initials: R
authors.lastname: De la Cruz-Mesía
authors.affiliation: Pontificia Universidad Católica de Chile, Santiago, Chile.
authors.forename: Fernando A
authors.initials: FA
authors.lastname: Quintana
authors.affiliation: University of Texas, Houston, USA.
authors.forename: Peter
authors.initials: P
authors.lastname: Müller
keywords: Dependent non-parametric model; Discriminant analysis; Longitudinal data; Markov chain Monte Carlo sampling; Non-parametric modelling; Random-effects models; Species sampling models