Semiparametric bayes joint modeling with functional predictors (Venue: GH seminar RM2)
Seminar Room 2, Newton Institute Gatehouse
We consider the problem of semiparametric Bayes joint modeling of predictors and a response variable, with a particular emphasis on functional predictors. Parametric models for the predictor and response are coupled through a joint distribution for subject-specific predictor and response coefficients. This joint distribution is assigned a flexible mixture prior, which allows the response distribution within predictor clusters to be unknown. To avoid label ambiguity and accelerate computation, we propose a combined sequential updating and Gibbs sampling algorithm for posterior computation. The methods are applied to data on women's weight gain during pregnancy and birth weight.