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...

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...