A moving average Cholesky factor model in covariance modelling for longitudinal data

作者：Zhang Weiping^*; Leng Chenlei

来源：Biometrika, 2012, 99(1): 141-150.

DOI：10.1093/biomet/asr068

• bic
• Longitudinal data analysis
• Maximum likelihood estimation
• Model selection
• Modified Cholesky decomposition
• Moving average

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.

• 出版日期2012-3
• 单位中国科学技术大学