Pairwise likelihood for the longitudinal mixed Rasch model

被引:13
|
作者
Feddag, M-L. [1 ]
Bacci, S. [2 ]
机构
[1] Univ Warwick, Dept Stat, Coventry CV4 7AL, W Midlands, England
[2] Univ Florence, Dept Stat, I-50134 Florence, Italy
关键词
GENERALIZED LINEAR-MODELS; MAXIMUM-LIKELIHOOD; PARAMETER-ESTIMATION; EM ALGORITHM; REGRESSION;
D O I
10.1016/j.csda.2008.08.031
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Inference in Generalized linear mixed models with multivariate random effects is often made cumbersome by the high-dimensional intractable integrals involved in the marginal likelihood. An inferential methodology based on the marginal pairwise likelihood approach is proposed. This method belonging to the broad class of composite likelihood involves marginal pairs probabilities of the responses which has analytical expression for the probit version of the model, from where we derived those of the logit version. The different results are illustrated with a simulation study and with an analysis of a real data from health-related quality of life. (C) 2008 Elsevier B.V. All rights reserved.
引用
收藏
页码:1027 / 1037
页数:11
相关论文
共 50 条
  • [41] Applying the Mixed Rasch Model to the Runco Ideational Behavior Scale
    Sen, Sedat
    [J]. CREATIVITY RESEARCH JOURNAL, 2016, 28 (04) : 426 - 434
  • [42] On weighting of bivariate margins in pairwise likelihood
    Joe, Harry
    Lee, Youngjo
    [J]. JOURNAL OF MULTIVARIATE ANALYSIS, 2009, 100 (04) : 670 - 685
  • [43] A mixed Rasch model of dual-process conditional reasoning
    Bonnefon, Jean-Francois
    Eid, Michael
    Vautier, Stephane
    Jmel, Said
    [J]. QUARTERLY JOURNAL OF EXPERIMENTAL PSYCHOLOGY, 2008, 61 (05): : 809 - 824
  • [44] Maximum pairwise-rank-likelihood-based inference for the semiparametric transformation model
    Yu, Tao
    Li, Pengfei
    Chen, Baojiang
    Yuan, Ao
    Qin, Jing
    [J]. JOURNAL OF ECONOMETRICS, 2023, 235 (02) : 454 - 469
  • [45] Standard and Novel Model Selection Criteria in the Pairwise Likelihood Estimation of a Mixture Model for Ordinal Data
    Ranalli, Monia
    Rocci, Roberto
    [J]. ANALYSIS OF LARGE AND COMPLEX DATA, 2016, : 53 - 68
  • [46] On approximate likelihood inference in a Poisson mixed model
    Sutradhar, BC
    Qu, ZD
    [J]. CANADIAN JOURNAL OF STATISTICS-REVUE CANADIENNE DE STATISTIQUE, 1998, 26 (01): : 169 - 186
  • [47] APPROXIMATING THE LIKELIHOOD OF THE MIXED MODEL FOR PEDIGREE DATA
    LANGE, K
    BOEHNKE, M
    [J]. AMERICAN JOURNAL OF HUMAN GENETICS, 1982, 34 (06) : A185 - A185
  • [48] Longitudinal Latent Markov Processes Observable Through an Invariant Rasch Model
    Bousseboua, Moussedek
    Mesbah, Mounir
    [J]. MATHEMATICAL AND STATISTICAL MODELS AND METHODS IN RELIABILITY: APPLICATIONS TO MEDICINE, FINANCE, AND QUALITY CONTROL, 2010, : 87 - +
  • [49] Likelihood Ratio Tests for Special Rasch Models
    Hessen, David J.
    [J]. JOURNAL OF EDUCATIONAL AND BEHAVIORAL STATISTICS, 2010, 35 (06) : 611 - 628
  • [50] LONGITUDINAL RASCH MODEL FOR SELF ASSESSMENT OF SIDE EFFECTS IN CHEMOTHERAPY CYCLES
    Pagani, Laura
    Zanarotti, Maria Chiara
    [J]. ELECTRONIC JOURNAL OF APPLIED STATISTICAL ANALYSIS, 2012, 5 (03) : 387 - 392