Quantile regression for longitudinal data based on latent Markov subject-specific parameters |
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Authors: | Alessio Farcomeni |
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Institution: | (1) Department of Quantitative Health Sciences, Cleveland Clinic, 9500 Euclid Avenue/JJN3-01, Cleveland, OH 44195, USA; |
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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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Keywords: | |
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