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A moving average Cholesky factor model in covariance modelling for longitudinal data
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Zhang, W. and Leng, Chenlei (2012) A moving average Cholesky factor model in covariance modelling for longitudinal data. Biometrika, Vol.99 (No.1). pp. 141-150. doi:10.1093/biomet/asr068 ISSN 0006-3444.
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Official URL: http://dx.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.
Item Type: | Journal Article | ||||
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Statistics | ||||
Journal or Publication Title: | Biometrika | ||||
Publisher: | Biometrika Trust | ||||
ISSN: | 0006-3444 | ||||
Official Date: | March 2012 | ||||
Dates: |
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Volume: | Vol.99 | ||||
Number: | No.1 | ||||
Page Range: | pp. 141-150 | ||||
DOI: | 10.1093/biomet/asr068 | ||||
Status: | Peer Reviewed | ||||
Publication Status: | Published | ||||
Access rights to Published version: | Restricted or Subscription Access |
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