arXiv subjects

Marco Alfó

Publications and source records attributed to Marco Alfó.

2 recordsLinked to original sources

arXiv2025

lqmix: an R package for longitudinal data analysis via linear quantile mixtures

The analysis of longitudinal data gives the chance to observe how unit behaviors change over time, but it also poses a series of issues. These have been the focus of an extensive literature in the context of linear and generalized linear regression moving also, in the last ten years or so, to the context of linear quantile regression for continuous responses. In this paper, we present \texttt{lqmix}, a novel \texttt{R} package that assists in estimating a class of linear quantile regression models for longitudinal data, in the presence of time-constant and/or time-varying, unit-specific, random coefficients, with unspecified distribution. Model parameters are estimated in a maximum likelihood framework via an extended EM algorithm, while parameters' standard errors are derived via a block-bootstrap procedure. The analysis of a benchmark dataset is used to give details on the package functions.

stat.CO↗

arXiv2015

Latent drop-out transitions in quantile regression

Longitudinal data are characterized by the dependence between observations coming from the same individual. In a regression perspective, such a dependence can be usefully ascribed to unobserved features (covariates) specific to each individual. On these grounds, random parameter models with time-constant or time-varying structure are well established in the generalized linear model context. In the quantile regression framework, specifications based on random parameters have only recently known a flowering interest. We start from the recent proposal by Farcomeni (2012) on longitudinal quantile hidden Markov models, and extend it to handle potentially informative missing data mechanism. In particular, we focus on monotone missingness which may lead to selection bias and, therefore, to unreliable inferences on model parameters. We detail the proposed approach by re-analyzing a well known dataset on the dynamics of CD4 cell counts in HIV seroconverters and by means of a simulation study.

stat.ME↗