Variable Selection for Joint Mean and Covariance Models via Penalized Likelihood

Kou, Chaofeng and Pan, Jianxin (2009) Variable Selection for Joint Mean and Covariance Models via Penalized Likelihood. [MIMS Preprint]

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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 2010, the AMS's Mathematics Subject Classification > 62 Statistics
Depositing User:	Ms Lucy van Russelt
Date Deposited:	09 Jul 2009
Last Modified:	08 Nov 2017 18:18
URI:	https://eprints.maths.manchester.ac.uk/id/eprint/1289

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