Fence Methods in Jointly Selecting Regression Variables and Covariance Structure for Longitudinal Autoregression Data

• Activity Number: 28
• Type: Contributed
• Date/Time: Sunday, July 29, 2012 : 2:00 PM to 3:50 PM
• Sponsor: Section on Nonparametric Statistics
• Author(s): Jiani Mou*+ and Jiming Jiang
• Companies: University of California at Davis and University of California at Davis
• Keywords: Fence Methods ; Two Stage Model Selection ; REML ; Covariance Structure ; Fixed Covariates ; Longitudinal Data

Abstract

Linear Mixed models are widely used in practice, but the literature of model selection is rather sparse. Fence method (Jiang et al. 2008) is a recently developed model selection strategy and has been proposed to solve nonconventional problems. In order to make Fence methods more suitable for a wide variety of problems, such as selecting models for longitudinal autoregression data not only on regression variables but also on the variance-covariance structure, we develop a Two-Step Fence procedure based on certain measure of lack of fit function, an adaptive constant c and REML idea. Methodology developments are supported by simulation studies and real data analyses.