With nonignorable missing responses, an efficient estimator and a variable selection method
for quantile regression coefficient are proposed based on smoothed weighted empirical …

This article develops a Bayesian approach for estimating panel quantile regression with
binary outcomes in the presence of correlated random effects. We construct a working …

Education has traditionally been classroom-oriented with a gradual growth of online courses
in recent times. However, the outbreak of the COVID-19 pandemic has dramatically …

Over the past decade, there has been growing enthusiasm for using electronic medical
records (EMRs) for biomedical research. Quantile regression estimates distributional …

In real applied fields such as clinical medicine, environmental sciences, psychology as well
as economics, we often encounter the task of conducting statistical inference for longitudinal …

This article describes an R package bqror that estimates Bayesian quantile regression for
ordinal models introduced in Rahman (2016). The paper classifies ordinal models into two …

Empirical studies in various fields, such as clinical trials, environmental sciences,
psychology, as well as finance and economics, often encounter the task of conducting …

In this paper, we propose a Bayesian quantile regression (QR) approach to jointly model
multivariate ordinal data. Firstly, a multivariate latent variable model is used to link the …

This paper investigates the joint modeling of mixed ordinal and continuous longitudinal
responses using a random effects model and applying a conditional approach. For the …

In this paper, we develop a joint quantile regression model for correlated mixed discrete and
continuous data using Gaussian copula. Our approach entails specifying marginal quantile …