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Generalized Linear Mixed Models Based on Latent Markov Heterogeneity Structures
Author(s) -
Farcomeni Alessio
Publication year - 2015
Publication title -
scandinavian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.359
H-Index - 65
eISSN - 1467-9469
pISSN - 0303-6898
DOI - 10.1111/sjos.12155
Subject(s) - mathematics , markov chain , random effects model , generalized linear mixed model , mixed model , constant (computer programming) , statistics , variable order markov model , markov model , econometrics , statistical physics , computer science , medicine , meta analysis , programming language , physics
We describe a generalized linear mixed model in which all random effects may evolve over time. Random effects have a discrete support and follow a first‐order Markov chain. Constraints control the size of the parameter space and possibly yield blocks of time‐constant random effects. We illustrate with an application to the relationship between health education and depression in a panel of adolescents, where the random effects are highly dimensional and separately evolve over time.