Abstract
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 EM-type algorithm based on appropriate recursions. Finally, we illustrate the model on a benchmark data set.
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Farcomeni, A. Quantile regression for longitudinal data based on latent Markov subject-specific parameters. Stat Comput 22, 141–152 (2012). https://doi.org/10.1007/s11222-010-9213-0
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DOI: https://doi.org/10.1007/s11222-010-9213-0