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. Con- straints 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 depres- sion in a panel of adolescents, where the random effects are highly dimensional and separately evolve over time.
Farcomeni, A. (2015). Generalized linear mixed models based on latent Markov heterogeneity structures. SCANDINAVIAN JOURNAL OF STATISTICS, 42(4), 1127-1135 [10.1111/sjos.12155].
Generalized linear mixed models based on latent Markov heterogeneity structures
FARCOMENI, Alessio
2015-01-01
Abstract
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. Con- straints 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 depres- sion in a panel of adolescents, where the random effects are highly dimensional and separately evolve over time.File | Dimensione | Formato | |
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