纵向数据中基于偏自相关的均值协方差同时建模（英文）

Joint Modeling of Mean-Covariance Structures Based on Partial Autocorrelation for Longitudinal Data

本文在纵向数据下提出一种均值-方差-相关矩阵的同时建模推断方法.通过应用偏相关系数,我们对相关系数矩阵进行无约束参数化,并且能够自动保证估计的相关系数矩阵满足正定性.在此基础上,我们对参数提出了一种回归推断方法,其具有简约性、可解释性和灵活性特点.实际数据分析和模拟研究表明了所提方法是有效的.

In this paper, we propose a joint mean-variance-correlation modeling approach for longitudinal studies. By applying partial autocorrelations, we obtain an unconstrained parametrization for the correlation matrix that automatically guarantees its positive definiteness, and develop a regression approach to model the correlation matrix of the longitudinal measurements by exploiting the parametrization. The proposed modeling framework is parsimonious, interpretable, and flexible for analyzing longitudinal data. Real data example and simulation support the effectiveness of the proposed approach.