Variable Selection and Inference Procedures for Marginal Analysis of Longitudinal Data with Missing Observations or Measurement Error

• Activity Number: 148
• Type: Invited
• Date/Time: Monday, August 5, 2013 : 10:30 AM to 12:20 PM
• Sponsor: SSC
• Author(s): Grace Y Yi*+
• Companies: University of Waterloo
• Keywords: longitudinal data ; marginal analysis ; measurement error ; missing data ; variable selection

Abstract

In contrast to extensive attention on model selection for univariate data, research on correlated data remains relatively limited. Furthermore, in the presence of missing data and/or measurement error, standard methods would typically break down. To address these issues, we propose marginal methods that simultaneously carry out model selection and estimation for longitudinal data analysis. Our methods have a number of appealing features: the applicability is broad because the methods are developed for a unied framework with marginal generalized linear models; model assumptions are minimal in that no full distribution is required for the response process and the distribution of the mismeasured covariates is left unspecified; and the implementation is straightforward. To justify the proposed methods, we provide both theoretical properties and numerical assessments.