A moving average Cholesky factor model in covariance modelling for longitudinal data
A moving average Cholesky factor model in covariance modelling for longitudinal data
复制标题
纵向数据协方差建模中的移动平均 Cholesky 因子模型
DOI：
10.1093/biomet/asr068
复制
发表时间：
2012-03-01
期刊：
影响因子：
2.7
通讯作者：
Leng, Chenlei
中科院分区：
文献类型：
--
作者：
Zhang, Weiping;Leng, Chenlei
我们提出了新的回归模型参数化的协方差结构在纵向数据分析。使用一种新的Cholesky因子，在这个分解的条目有一个移动平均和对数创新的解释，并建模为协变量的线性函数。我们提出了有效的最大似然估计的联合均值-协方差分析的基础上，这种分解，并推导出渐近分布的系数估计。在此基础上，研究了一种基于bic的局部搜索模型选择算法，该算法比传统的全子集选择算法计算效率更高，并证明了该算法的模型选择一致性。从而验证了Pan &麦肯齐（2003）的一个猜想。我们通过对CD 4轨迹的数据分析和模拟，证明了该方法的有限样本性能。版权所有2012，牛津大学出版社。
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. Copyright 2012, Oxford University Press.