This paper investigates the identification of quantiles and quantile regression parameters when observations are set valued. We define random sets in a way that extends definition for regular variables. then give sharp characterization this by extending concepts from theory. Applying its sharpness to parametric models yields sharpness. apply our methods data on localized environmental benefits ...

Regression models based on the log-symmetric family of distributions are particularly useful when response variable is continuous, positive, and asymmetrically distributed. In this article, we propose derive a class new approach to quantile regression using parameterized by means their quantiles. Two Monte Carlo simulation studies conducted utilizing R software. The first one analyzes performan...

In many observational longitudinal studies, the outcome of interest presents a skewed distribution, is subject to censoring due to detection limit or other reasons, and is observed at irregular times that may follow a outcome-dependent pattern. In this work, we consider quantile regression modeling of such longitudinal data, because quantile regression is generally robust in handling skewed and...

1. Introduction: Recent work by Schennach(2005) has opened the way to a Bayesian treatment of quantile regression. Her method, called Bayesian exponentially tilted empirical likelihood (BETEL), provides a likelihood for data y subject only to a set of m moment conditions of the form Eg(y, θ) = 0 where θ is a k dimensional parameter of interest and k may be smaller, equal to or larger than m. Th...

In this paper, we propose a new robust estimator for regression problems in the form of the linear combination of quantile regressions. The proposed robust regression estimator is helpful for the conditional mean estimation especially when the error distribution is asymmetric or/and heteroscedastic, where conventional robust regressions yield considerable bias to the conditional mean. First we ...