title: Robust estimation in mixed linear models with non-monotone missingness.
abstract: We introduce a model to account for abrupt changes among repeated measures with non-monotone missingness. Development of likelihood inferences for such models is hard because it involves intractable integration to obtain the marginal likelihood. We use hierarchical likelihood to overcome such difficulty. Abrupt changes among repeated measures can be well described by introducing random effects in the dispersion. A simulation study shows that the resulting estimator is efficient, robust against misspecification of fatness of tails. For illustration we use a schizophrenic behaviour data presented by Rubin and Wu.
authors.affiliation: Clinical Research Center, ASAN Medical Center, Seoul 138-736, Korea. ysch@statcom.snu.ac.kr
authors.forename: Sungcheol
authors.initials: S
authors.lastname: Yun
authors.forename: Youngjo
authors.initials: Y
authors.lastname: Lee
mesh_terms: D003198:Computer Simulation; D005133:Eye Movements / Q000502:physiology; D005260:Female; D006801:Humans; D016013:Likelihood Functions; D016014:Linear Models; D008137:Longitudinal Studies; D008297:Male; D012559:Schizophrenia / Q000503:physiopathology