Advances in Functional Mixed Models

• Activity Number: 406
• Type: Topic Contributed
• Date/Time: Tuesday, August 6, 2013 : 2:00 PM to 3:50 PM
• Sponsor: Biometrics Section
• Author(s): Tapabrata Maiti*+ and Samiran Sinha and Ping-Shou Zhong
• Companies: Michigan State University and Texas A&M University and Michigan State University
• Keywords: Best linear unbiased predictor ; B-splines ; Functional data analysis ; Linear mixed model ; Mean squared error

Abstract

Functional data analysis has become an important area of research due to its ability of handling high dimensional and complex data structure. However, the development is limited in the context of linear mixed models. In this article we consider a varying coefficient linear mixed model. The varying coefficients are semi parametrically modeled via B-splines. We propose a method of estimation of fixed effect parameters and prediction of random effects that can be implemented using standard softwares. Further, we derive the mean square errors of prediction, and propose a way of estimating the mean squared errors along their accepted level of validity. In the sequel, we develop the theory to validate our methods that enriches functional linear mixed model. The procedure is illustrated with simulation and real data examples.