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A Moving Average Cholesky Factor Model in Covariance Modeling for Longitudinal Data
澳门新葡8455最新网站:2011年10月26日 00:00 点击数:

报告人:Leng Chenlei

报告地点:澳门新葡8455最新网站104室

报告澳门新葡8455最新网站:2011年10月28日(星期五)10:00

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报告摘要:

We propose new regression models for parameterising 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 modeled 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 and Mackenzie (2003) is verified. We demonstrate the finite-sample performance of the proposed method via analysis of the data on CD4 trajectories and through simulations. This is a joint work with Weiping Zhang from USTC.

主讲人概况:

新加坡国立大学概率与统计系

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