A Composite Likelihood Method for Analysis of Clustered Data With/Without Missing Observations

• Activity Number: 537
• Type: Invited
• Date/Time: Wednesday, August 6, 2014 : 2:00 PM to 3:50 PM
• Sponsor: ENAR
• Author(s): Wenqing He*+ and Grace Yi
• Companies: University of Western Ontario and University of Waterloo
• Keywords: correlated data ; partially linear models ; missing data ; pairwise likelihood ; single-index models

Abstract

Clustered data arise commonly in practice and it is often of interest to estimate the mean response parameters as well as the association parameters. However, much research has been directed to address the mean response parameters with the association parameters relegated to a nuisance role. There is relatively little work concerning both the marginal and association structures, especially in the semiparametric framework. In this talk, I will discuss semiparametric methods for analyzing clustered data with or without missing observations. The proposed methodology is evaluated through numerical studies.