A Flexible Class of Models for Longitudinal Data Subject to Data Irregularities

• Activity Number: 80
• Type: Contributed
• Date/Time: Sunday, July 29, 2012 : 4:00 PM to 5:50 PM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): Liwei Wang*+ and Sujit Kumar Ghosh
• Companies: North Carolina State University and North Carolina State University
• Keywords: Gaussian process ; Longitudinal data ; Mixed effects model ; MCMC methods

Abstract

Analysis of longitudinal data within a mixed model framework becomes a challenging task when observations are subject to data irregularities like censoring and missing values. Often finite dimensional (parametric) models are found inadequate to address the complex relationship between the response and predictors. A majority of the currently available models and associated estimation methodologies are based on restrictive assumptions on the correlation structure of longitudinal data. To begin with we develop a flexible class of models based on a sequence of Bernstein polynomials with varying degrees and propose a model fitting mechanism assuming fully observed data. Various simulated data scenarios are used to illustrate the superior performance of the proposed estimation methodology. We then extend the estimation methodology to accommodate the data irregularities using a Markov Chain Monte Carlo based approach. The newly proposed models and associated inference methodologies are illustrated using real data analysis.