Generalized Linear Mixed Models Based on Latent Markov Heterogeneity Structures
Scandinavian Journal of Statistics, 2015, vol. 42, issue 4, 1127-1135
type="main" xml:id="sjos12155-abs-0001"> 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.
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