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Quantile regression for longitudinal data based on latent Markov subject-specific parameters. (English) Zbl 1322.62206

Summary: We propose a latent Markov quantile regression model for longitudinal data with non-informative drop-out. The observations, conditionally on covariates, are modeled through an asymmetric Laplace distribution. Random effects are assumed to be time-varying and to follow a first order latent Markov chain. This latter assumption is easily interpretable and allows exact inference through an ad hoc EMtype algorithm based on appropriate recursions. Finally, we illustrate the model on a benchmark data set.

MSC:

62M05 Markov processes: estimation; hidden Markov models
62G30 Order statistics; empirical distribution functions
62J02 General nonlinear regression

Software:

R
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