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|Title:||A moving average Cholesky factor model in covariance modelling for longitudinal data||Authors:||Zhang, W.
Longitudinal data analysis
Maximum likelihood estimation
Modified Cholesky decomposition
|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|
|Appears in Collections:||Staff Publications|
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