You are here: MIMS > EPrints
MIMS EPrints

2009.49: Variable Selection for Joint Mean and Covariance Models via Penalized Likelihood

2009.49: Chaofeng Kou and Jianxin Pan (2009) Variable Selection for Joint Mean and Covariance Models via Penalized Likelihood.

Full text available as:

PDF - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
193 Kb

Abstract

In this paper, we propose a penalized maximum likelihood method for variable selection in joint mean and covariance models for longitudinal data. Under certain regularity conditions, we establish the consistency and asymptotic normality of the penalized maximum likelihood estimators of parameters in the models. We further show that the proposed estimation method can correctly identify the true models, as if the true models would be known in advance. We also carry out real data analysis and simulation studies to assess the small sample performance of the new procedure, showing that the proposed variable selection method works satisfactorily

Item Type:MIMS Preprint
Uncontrolled Keywords:Cholesky decomposition; Joint mean-covariance models; Longitudinal data; Penalized maximum likelihood; Variable selection.
Subjects:MSC 2000 > 62 Statistics
MIMS number:2009.49
Deposited By:Ms Lucy van Russelt
Deposited On:09 July 2009

Download Statistics: last 4 weeks
Repository Staff Only: edit this item