Censored regression quantiles

✍ James L. Powell 📂 Article 📅 1986 🏛 Elsevier Science 🌐 English ⚖ 775 KB

The object of this paper is to demonstrate how the LAD estimation method for the censored regression model can be extended to more general quantiles. In this paper. the fo:m of the conditional quantiles for the censored regression models is heuristically derived and discussed. The resulting estimato

Further improvements in the calculation

Further improvements in the calculation of Censored Quantile Regressions

✍ Mehdi Hosseinkouchack 📂 Article 📅 2011 🏛 Elsevier Science 🌐 English ⚖ 319 KB

are very powerful inferencing tools in economics and engineering. As the calculation of censored quantile regressions involves minimizing a nonconvex and nondifferentiable function, global optimization techniques can be the only breakthroughs. The first implementation of a global optimization techni

Restricted Regression Quantiles

✍ Quanshui Zhao 📂 Article 📅 2000 🏛 Elsevier Science 🌐 English ⚖ 263 KB

Regression quantiles can be used as prediction intervals for the response variable. But such applications are often hampered by the problem of quantile crossing in finite sample cases. This article examines the efficiency properties of restricted regression quantiles that are proposed by X. He (1997

Smoothed binary regression quantiles

✍ Gregory Kordas 📂 Article 📅 2006 🏛 John Wiley and Sons 🌐 English ⚖ 261 KB

## Abstract This paper extends results regarding smoothed median binary regression to general smoothed binary quantile regression, discusses the interpretation of the resulting estimators under alternative assumptions, and shows how they may be used to obtain semiparametric estimates of counterfact

Nonparametric regression M-quantiles

✍ J. Antoch; P. Janssen 📂 Article 📅 1989 🏛 Elsevier Science 🌐 English ⚖ 449 KB

Bayesian quantile regression methods

✍ Tony Lancaster; Sung Jae Jun 📂 Article 📅 2009 🏛 John Wiley and Sons 🌐 English ⚖ 384 KB

## Abstract This paper is a study of the application of Bayesian exponentially tilted empirical likelihood to inference about quantile regressions. In the case of simple quantiles we show the exact form for the likelihood implied by this method and compare it with the Bayesian bootstrap and with Je