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Generalized Linear Mixed Model for Multivariate Multilevel Binomial Data
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 Title & Authors
Generalized Linear Mixed Model for Multivariate Multilevel Binomial Data
Lim, Hwa-Kyung; Song, Seuck-Heun; Song, Ju-Won; Cheon, Soo-Young;
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We are likely to face complex multivariate data which can be characterized by having a non-trivial correlation structure. For instance, omitted covariates may simultaneously affect more than one count in clustered data; hence, the modeling of the correlation structure is important for the efficiency of the estimator and the computation of correct standard errors, i.e., valid inference. A standard way to insert dependence among counts is to assume that they share some common unobservable variables. For this assumption, we fitted correlated random effect models considering multilevel model. Estimation was carried out by adopting the semiparametric approach through a finite mixture EM algorithm without parametric assumptions upon the random coefficients distribution.
GLMM;multi-level;correlated random effects;NPML;
 Cited by
남아 출생률 자료에 대한 이질성 분석,임화경;송석헌;송주원;

응용통계연구, 2009. vol.22. 2, pp.365-373 crossref(new window)
Abramowitz, M. and Stegun, I. A. (1972). Handbook of Mathematical Functions, Dover, New York

Aitkin, M. (1999). A general maximum likelihood analysis of variance components in generalized linear models, Biometrics, 55, 117-128 crossref(new window)

Aitkin, M., Anderson, D. and Hinde, J. (1981). Statistical modelling of data on teaching styles, Journal of the Royal Statistical Society, Series A, 144, 419-461 crossref(new window)

Aitkin, M. and Longford, N. T. (1986). Statistical modelling issues in school effectiveness studies, Journal of the Royal Statistical Society, Series A, 149, 1-43

Goldstein, H. (1995). Multilevel Statistical Models, Edward Arnold, London

Golub, G. H. and Welsch, J. H. (1969). Calculation of Gaussian quadrature rules, Mathematics of Compu-tation, 23, 221-230 crossref(new window)

Green, P. J. (1995). Reversible jump Markov chain Monte Carlo computation and Bayesian model determi- nation, Biometrika, 82, 711-732 crossref(new window)

Kreft, I. G. G. and de Leeuw, J. (1998). Introducing Multilevel Modeling, Sage Publications, London

McLachlan, G. and Peel, D. (2000). Finite Mixture Models, John Wiley & Sons, New York

Petris, G. and Tardella, L. (2003). A geometric approach to transdimensional MCMC, The Canadian Journal of Statistics/La Revue Canadienne de Statistique, 31, 469-482 crossref(new window)

Raudenbush, S. W. and Bryk, A. S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods, 2nd Edition, Sage, London

Schall, R. (1991). Estimation in generalized linear models with random effects, Biometrika, 78, 719-727 crossref(new window)

Snijders, T. A. B. and Bosker, R. J. (1999). Multilevel Analysis: An Introduction to Basic and Advanced Multilevel Modelling, Sage, London