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Joint Variable Selection of Mean-Covariance Model for Longitudinal Data

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DOI: 10.4236/ojs.2013.31004    2,971 Downloads   5,114 Views   Citations

ABSTRACT

In this paper we reparameterize covariance structures in longitudinal data analysis through the modified Cholesky decomposition of itself. Based on this modified Cholesky decomposition, the within-subject covariance matrix is decomposed into a unit lower triangular matrix involving moving average coefficients and a diagonal matrix involving innovation variances, which are modeled as linear functions of covariates. Then, we propose a penalized maximum likelihood method for variable selection in joint mean and covariance models based on this decomposition. Under certain regularity conditions, we establish the consistency and asymptotic normality of the penalized maximum likelihood estimators of parameters in the models. Simulation studies are undertaken to assess the finite sample performance of the proposed variable selection procedure.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

D. Xu, Z. Zhang and L. Wu, "Joint Variable Selection of Mean-Covariance Model for Longitudinal Data," Open Journal of Statistics, Vol. 3 No. 1, 2013, pp. 27-35. doi: 10.4236/ojs.2013.31004.

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