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Robust estimation in mixed linear models with non‐monotone missingness
Author(s) -
Yun Sungcheol,
Lee Youngjo
Publication year - 2005
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.2479
Subject(s) - missing data , estimator , monotone polygon , maximum likelihood , econometrics , statistics , computer science , random effects model , estimation , marginal likelihood , mathematics , medicine , economics , meta analysis , geometry , management
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. Copyright © 2005 John Wiley & Sons, Ltd.