False discovery rate;
Importance sampling;
Incomplete data;
Linear mixed model;
Longitudinal study;
Maximum likelihood;
Proteomics experiment;
SENSITIVITY-ANALYSIS;
DATA MECHANISM;
MIXED MODELS;
DROP-OUT;
REGRESSION;
NONRESPONSE;
D O I:
10.1016/j.csda.2013.10.027
中图分类号:
TP39 [计算机的应用];
学科分类号:
081203 ;
0835 ;
摘要:
For the analysis of longitudinal data with nonignorable and nonmonotone missing responses, a full likelihood method often requires intensive computation, especially when there are many follow-up times. The authors propose and explore a Monte Carlo method, based on importance sampling, for approximating the maximum likelihood estimators. The finite-sample properties of the proposed estimators are studied using simulations. An application of the proposed method is also provided using longitudinal data on peptide intensities obtained from a proteomics experiment of trauma patients. (C) 2013 Elsevier B.V. All rights reserved.
机构:
Univ Waterloo, Dept Stat & Actuarial Sci, Waterloo, ON, CanadaUniv Waterloo, Dept Stat & Actuarial Sci, Waterloo, ON, Canada
Zhao, Puying
Wang, Lei
论文数: 0引用数: 0
h-index: 0
机构:
Nankai Univ, LPMC, Tianjin, Peoples R China
Nankai Univ, Inst Stat, Tianjin, Peoples R China
Univ Wisconsin, Dept Stat, Madison, WI 53706 USAUniv Waterloo, Dept Stat & Actuarial Sci, Waterloo, ON, Canada
Wang, Lei
Shao, Jun
论文数: 0引用数: 0
h-index: 0
机构:
Univ Wisconsin, Dept Stat, Madison, WI 53706 USAUniv Waterloo, Dept Stat & Actuarial Sci, Waterloo, ON, Canada
机构:
Peking Univ, Dept Probabil & Stat, Beijing, Peoples R ChinaPeking Univ, Dept Probabil & Stat, Beijing, Peoples R China
Li, Yilin
Miao, Wang
论文数: 0引用数: 0
h-index: 0
机构:
Peking Univ, Dept Probabil & Stat, Beijing, Peoples R China
Peking Univ, Dept Probabil & Stat, Beijing 100871, Peoples R ChinaPeking Univ, Dept Probabil & Stat, Beijing, Peoples R China
Miao, Wang
Shpitser, Ilya
论文数: 0引用数: 0
h-index: 0
机构:
Johns Hopkins Univ, Dept Comp Sci, Baltimore, MD USAPeking Univ, Dept Probabil & Stat, Beijing, Peoples R China
Shpitser, Ilya
Tchetgen, Eric J. Tchetgen J.
论文数: 0引用数: 0
h-index: 0
机构:
Univ Penn, Dept Stat, Wharton Sch, Philadelphia, PA USAPeking Univ, Dept Probabil & Stat, Beijing, Peoples R China