Please use this identifier to cite or link to this item: https://doi.org/10.1093/biomet/asr068
Title: A moving average Cholesky factor model in covariance modelling for longitudinal data
Authors: Zhang, W.
Leng, C. 
Keywords: bic
Longitudinal data analysis
Maximum likelihood estimation
Model selection
Modified Cholesky decomposition
Moving average
Issue Date: Mar-2012
Citation: Zhang, W., Leng, C. (2012-03). A moving average Cholesky factor model in covariance modelling for longitudinal data. Biometrika 99 (1) : 141-150. ScholarBank@NUS Repository. https://doi.org/10.1093/biomet/asr068
Abstract: We propose new regression models for parameterizing covariance structures in longitudinal data analysis. Using a novel Cholesky factor, the entries in this decomposition have a moving average and log-innovation interpretation and are modelled as linear functions of covariates. We propose efficient maximum likelihood estimates for joint mean-covariance analysis based on this decomposition and derive the asymptotic distributions of the coefficient estimates. Furthermore, we study a local search algorithm, computationally more efficient than traditional all subset selection, based on bic for model selection, and show its model selection consistency. Thus, a conjecture of Pan & MacKenzie (2003) is verified. We demonstrate the finite-sample performance of the method via analysis of data on CD4 trajectories and through simulations. © 2011 Biometrika Trust.
Source Title: Biometrika
URI: http://scholarbank.nus.edu.sg/handle/10635/104942
ISSN: 00063444
DOI: 10.1093/biomet/asr068
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