一种离散纵向数据相依结构建模的Cholesky因子模型  

A Cholesky factor model in correlation modeling for discrete longitudinal data

作　　者：李叶蓁 张伟平[1] LI Yezhen;ZHANG Weiping(Department of Statistics and Finance, School of Management, University of Science and Technology of China, Hefei 230026, China)

机构地区：[1]中国科学技术大学管理学院统计与金融系,安徽合肥230026

出　　处：《中国科学技术大学学报》2020年第9期1266-1276,共11页JUSTC

基　　金：the NSFC of China(11671374,71771203,71631006).

摘　　要：对一类响应变量为离散型的平衡或非平衡纵向数据,提出了均值-相关系数联合回归模型框架,并且使用Cholesky分解方法对模型的相关结构进行参数化,使其具有良好的统计解释性.为了解决似然推断中高维积分计算的难题,提出了一种高效的蒙特卡罗期望最大化(MCEM)算法,并证明了参数估计的渐近性质.模拟实验和实际数据分析表明提出的方法是高度有效的.A joint mean-correlation regression model framework was proposed for a family of generic discrete responses either balanced or unbalanced,and a Cholesky decomposition method was used for statistically meaningful reparameterization of correlation structures.To overcome computational intractability in maximizing the full likelihood function of the model,a computationally efficient Monte Carlo expectation maximization(MCEM)approach was proposed.Theoretical properties were also established for the resulting estimators.Simulation studies and a real data analysis show that the proposed approach yields highly efficient estimators for the parameters.

关 键 词：离散纵向数据 CHOLESKY分解 均值-相关系数回归模型 蒙特卡罗期望最大化算法 

分 类 号：O212.1[理学—概率论与数理统计]