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

Authors:
Alessio Farcomeni
Affiliations:
Sapienza-University of Rome, Rome, Italy
Venue:
Statistics and Computing
Year:
2012

Citing 1
Cited 1

Quantile regression for longitudinal data

Journal of Multivariate Analysis - Special issue on semiparametric and nonparametric mixed models

Quantile regression for longitudinal data with a working correlation model

Computational Statistics & Data Analysis

Quantified Score

Hi-index	0.00

Visualization

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.