Bayesian estimation and influence diagnostics of generalized partially linear mixed-effects models for longitudinal data

被引:4
作者
Duan, Xing-De [1 ,2 ]
Tang, Nian-Sheng [1 ]
机构
[1] Yunnan Univ, Dept Stat, Kunming 650091, Peoples R China
[2] Chuxiong Normal Sch, Inst Appl Stat, Chuxiong 675000, Peoples R China
基金
高等学校博士学科点专项科研基金;
关键词
Bayesian case deletion influence; Cook's posterior mean distance; Fisher's iterative scoring algorithm; generalized partial linear mixed models; phi-divergence; 62H12; 62F15; DELETION DIAGNOSTICS; ESTIMATING EQUATIONS; ROBUST ESTIMATION; REGRESSION; LIKELIHOOD; INFERENCE;
D O I
10.1080/02331888.2015.1078332
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
This paper develops a Bayesian approach to obtain the joint estimates of unknown parameters, nonparametric functions and random effects in generalized partially linear mixed models (GPLMMs), and presents three case deletion influence measures to identify influential observations based on the phi-divergence, Cook's posterior mean distance and Cook's posterior mode distance of parameters. Fisher's iterative scoring algorithm is developed to evaluate the posterior modes of parameters in GPLMMs. The first-order approximation to Cook's posterior mode distance is presented. The computationally feasible formulae for the phi-divergence diagnostic and Cook's posterior mean distance are given. Several simulation studies and an example are presented to illustrate our proposed methodologies.
引用
收藏
页码:525 / 539
页数:15
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