Multilevel cumulative logistic regression model with random effects: Application to British social attitudes panel survey data

被引：6

作者：

Chan, Moon-tong ^{[1
]}

Yu, Dalei ^{[2
,3
]}

Yau, Kelvin K. W. ^{[1
]}

机构：

[1] City Univ Hong Kong, Dept Management Sci, Hong Kong, Hong Kong, Peoples R China

[2] Yunnan Univ Finance & Econ, Stat & Math Coll, Kunming 650221, Peoples R China

[3] Yunnan Tongchuang Sci Comp & Data Min Ctr, Kunming 650221, Peoples R China

来源：

COMPUTATIONAL STATISTICS & DATA ANALYSIS | 2015年 / 88卷

基金：

中国国家自然科学基金;

关键词：

Generalized linear mixed model; Multilevel model; Ordinal response; Random effect; CONDITIONAL AKAIKE INFORMATION; LINEAR MIXED MODELS; REML ESTIMATION; INFERENCE; SELECTION;

D O I：

10.1016/j.csda.2015.02.018

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

A multilevel model for ordinal data in generalized linear mixed models (GLMM) framework is developed to account for the inherent dependencies among observations within clusters. Motivated by a data set from the British Social Attitudes Panel Survey (BSAPS), the random district effects and respondent effects are incorporated into the linear predictor to accommodate the nested clusterings. The fixed (random) effects are estimated (predicted) by maximizing the penalized quasi likelihood (PQL) function, whereas the variance component parameters are obtained via the restricted maximum likelihood (REML) estimation method. The model is employed to analyze the BSAPS data. Simulation studies are conducted to assess the performance of estimators. (C) 2015 Elsevier B.V. All rights reserved.

引用

页码：173 / 186

页数：14

共 38 条

[1] Allen R. G., 1998, FAO Irrigation and Drainage Paper
[2] [Anonymous], 2001, Quantitative Models in Marketing Research
[3] Standard errors of prediction in generalized linear mixed models
Booth, JG
Hobert, JP
[J]. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 1998, 93 (441) : 262 - 272
[4] APPROXIMATE INFERENCE IN GENERALIZED LINEAR MIXED MODELS
BRESLOW, NE
CLAYTON, DG
[J]. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 1993, 88 (421) : 9 - 25
[5] Bayesian covariance selection in generalized linear mixed models
Cai, Bo
Dunson, David B.
[J]. BIOMETRICS, 2006, 62 (02) : 446 - 457
[6] Mixed models for ordinal data: A pharmacokinetic study on the effectiveness of drug for the reduction of epileptic seizures
Chakraborty, Arindom
Das, Kalyan
[J]. STATISTICS IN MEDICINE, 2008, 27 (18) : 3490 - 3502
[7] Model selection for multi-component frailty models
Do Ha, Il
Lee, Youngjo
MacKenzie, Gilbert
[J]. STATISTICS IN MEDICINE, 2007, 26 (26) : 4790 - 4807
[8] Conditional Akaike information under generalized linear and proportional hazards mixed models
Donohue, M. C.
Overholser, R.
Xu, R.
Vaida, F.
[J]. BIOMETRIKA, 2011, 98 (03) : 685 - 700
[9] Generalized linear mixed models for ordered responses in complex multilevel structures: effects beneath the school or college in education
Fielding, A
Yang, M
[J]. JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES A-STATISTICS IN SOCIETY, 2005, 168 : 159 - 183
[10] AN APPROXIMATE GENERALIZED LINEAR-MODEL WITH RANDOM EFFECTS FOR INFORMATIVE MISSING DATA
FOLLMANN, D
WU, M
[J]. BIOMETRICS, 1995, 51 (01) : 151 - 168

← 1 2 3 4 →