Quantile regression for longitudinal data based on latent Markov subject-specific parameters

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 cha...

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Bibliographic Details
Published inStatistics and computing Vol. 22; no. 1; pp. 141 - 152
Main Author Farcomeni, Alessio
Format Journal Article
LanguageEnglish
Published Boston Springer US 01.01.2012
Subjects
Artificial Intelligence
Mathematics and Statistics
Probability and Statistics in Computer Science
Statistical Theory and Methods
Statistics
Statistics and Computing/Statistics Programs
Hidden Markov model
Longitudinal data
Quantile regression
Asymmetric Laplace distribution
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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 EM-type algorithm based on appropriate recursions. Finally, we illustrate the model on a benchmark data set.
ISSN:0960-3174
1573-1375
DOI:10.1007/s11222-010-9213-0