Generalized Linear Mixed Models Based on Latent Markov Heterogeneity Structures

摘要

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.