Generalized Linear Mixed Model for Multivariate Multilevel Binomial Data

다변량 다수준 이항자료에 대한 일반화선형혼합모형

Lim, Hwa-Kyung;Song, Seuck-Heun;Song, Ju-Won;Cheon, Soo-Young

  • Published : 2008.12.31


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


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