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Local influence diagnostics for hierarchical finite‐mixture random‐effects models
Author(s) -
Rakhmawati Trias Wahyuni,
Molenberghs Geert,
Verbeke Geert,
Faes Christel
Publication year - 2018
Publication title -
biometrical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.108
H-Index - 63
eISSN - 1521-4036
pISSN - 0323-3847
DOI - 10.1002/bimj.201600203
Subject(s) - random effects model , multilevel model , mixed model , mathematics , mixture model , hierarchical database model , econometrics , generalized linear mixed model , perturbation (astronomy) , maximum likelihood , statistical physics , statistics , computer science , data mining , physics , meta analysis , medicine , quantum mechanics
The main objective of this paper is to evaluate the influence of individual subjects exerted on a random‐effects model for repeated measures, where the random effects follow a mixture distribution. The diagnostic tool is based on local influence with perturbation scheme that explicitly targets influences resulting from perturbing the mixture component probabilities. Bruckers, Molenberghs, Verbeke, and Geys (2016) considered a similar model, but focused on influences stemming from perturbing a subject's likelihood contributions as a whole. We also compare the two types of perturbation. Our results are illustrated using linear mixed models fitted to data from three studies. A simulation study is also conducted in order to strengthen the result from case studies.