Traditionally, assessing the accuracy of inference based on regression quantiles has relied
on the Bahadur representation. This provides an error of order n^-1/4 in normal …

This paper makes two main contributions. First, we construct “density-free” confidence
intervals and confidence bands for conditional quantiles in nonparametric and …

A collection of quantile curves provides a complete picture of conditional distributions. A
properly centered and scaled version of the estimated curves at various quantile levels gives …

This report provides a short and generally accessible overview of the technique of quantile
regression, with focus on introducing the method and discussing some major applications …

Quantile regression offers a more complete statistical model than mean regression and now
has widespread applications. Consequently, we provide a review of this technique. We …

Quantile estimation has attracted significant research interest in recent years. However,
there has been only a limited literature on quantile estimation in the presence of incomplete …

Recently, He and Zhu (2003) derived an omnibus goodness-of-fit test for linear or nonlinear
quantile regression models based on a CUSUM process of the gradient vector, and they …

Regression quantiles provide a natural and powerful approach for robust analysis of the
general linear model. However, departures from independence and stationarity of the errors …

Conventional regression techniques focus on the conditional averages, but often of more
interest are lower or upper conditional quantiles. A more informative description of the …

ABSTRACT A two-stage approach is proposed to overcome the problem in quantile
regression, where separately fitted curves for several quantiles may cross. The standard …