TY - JOUR
T1 - Finite mixture regression model with random effects
T2 - Application to neonatal hospital length of stay
AU - Yau, Kelvin K.W.
AU - Lee, Andy H.
AU - Ng, Angus S.K.
PY - 2003/1/28
Y1 - 2003/1/28
N2 - A two-component mixture regression model that allows simultaneously for heterogeneity and dependency among observations is proposed. By specifying random effects explicitly in the linear predictor of the mixture probability and the mixture components, parameter estimation is achieved by maximising the corresponding best linear unbiased prediction type log-likelihood. Approximate residual maximum likelihood estimates are obtained via an EM algorithm in the manner of generalised linear mixed model (GLMM). The method can be extended to a g-component mixture regression model with the component density from the exponential family, leading to the development of the class of finite mixture GLMM. For illustration, the method is applied to analyse neonatal length of stay (LOS). It is shown that identi
AB - A two-component mixture regression model that allows simultaneously for heterogeneity and dependency among observations is proposed. By specifying random effects explicitly in the linear predictor of the mixture probability and the mixture components, parameter estimation is achieved by maximising the corresponding best linear unbiased prediction type log-likelihood. Approximate residual maximum likelihood estimates are obtained via an EM algorithm in the manner of generalised linear mixed model (GLMM). The method can be extended to a g-component mixture regression model with the component density from the exponential family, leading to the development of the class of finite mixture GLMM. For illustration, the method is applied to analyse neonatal length of stay (LOS). It is shown that identi
KW - EM algorithm
KW - Generalised linear mixed models
KW - Heterogeneity
KW - Mixture distributions
KW - Random effects
UR - http://www.scopus.com/inward/record.url?scp=0037469109&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-0037469109&origin=recordpage
U2 - 10.1016/S0167-9473(02)00180-9
DO - 10.1016/S0167-9473(02)00180-9
M3 - RGC 21 - Publication in refereed journal
SN - 0167-9473
VL - 41
SP - 359
EP - 366
JO - Computational Statistics and Data Analysis
JF - Computational Statistics and Data Analysis
IS - 3-4
ER -