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.
Quantile regression for longitudinal data based on latent Markov subject-specific parameters / Farcomeni, Alessio. - In: STATISTICS AND COMPUTING. - ISSN 0960-3174. - 22:1(2012), pp. 141-152. [10.1007/s11222-010-9213-0]
Quantile regression for longitudinal data based on latent Markov subject-specific parameters
FARCOMENI, Alessio
2012
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.