Bayesian Nonparametric Longitudinal Data Analysis with Embedded Autoregressive Structure: Application to Hormone Data

• Activity Number: 553
• Type: Invited
• Date/Time: Wednesday, August 4, 2010 : 2:00 PM to 3:50 PM
• Sponsor: Section on Nonparametric Statistics
• Author(s): Wesley Johnson*+ and Fernando Quintana
• Companies: University of California, Irvine and Pontificia Universidad Católica de Chile
• Keywords: Longitudinal Data ; Autogregressive Model ; Bayes ; Covariance Estimation ; Function Estimation ; Markov chain Monte Carlo

Abstract

We develop a novel Dirichlet Process Mixture model for irregular longitudinal data. The model mixes on the two parameters of the traditional Ornstein-Uhlenbeck process with exponential covariance function and thus allows for the possibility of multiple groups with distinct autoregressive covariance structures. We illustrate the use of the model to track hormone curve data through the menopausal transition, and we also test the model on simulated data, both to check its performance in estimating mean functions as well as a variety of covariance structures.