Quantile regression (QR) models are one of the methods for longitudinal data analysis. When responses seemto be skew and asymmetric due to outliers heavy-tails, QR may work suitably. This paper developes semi-parametric quantile model analyzing continuous ordinal mixed responses. The latent variable some threshold parameters used perform model’s part. error has Asymmetric Laplace (AL) distribut...

In this paper, minimal conditions under which a semi-parametric binary response model is identified in a Bayesian framework are presented and compared to the conditions usually required in a sampling theory framework. Running headline: Semi-parametric Binary Response Models.

Multi-state Markov models have proven useful in many concrete situations and have had successful applications, notably in analysing disease history data. Continuous-time Markov models with three transient states are considered for the study of asthma control evolution in a cohort of 406 patients with persistent asthma. Firstly, the model is considered in the homogeneous case. Covariates are tak...

Drop-out is a prevalent complication in the analysis of data from longitudinal studies, and remains an active area of research for statisticians and other quantitative methodologists. This tutorial is designed to synthesize and illustrate the broad array of techniques that are used to address outcome-related drop-out, with emphasis on regression-based methods. We begin with a review of importan...

Integer-valued autoregressive (INAR) time series form a very useful class of processes suitable to model time series of counts. In the common formulation of Du and Li (1991, JTSA), INAR models of order p share the autocorrelation structure with classical autoregressive time series. This fact allows to estimate the INAR coefficients, e.g., by Yule-Walker estimators. However, contrary to the AR c...