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March, 1992 Regression Rank Scores and Regression Quantiles
C. Gutenbrunner, J. Jureckova
Ann. Statist. 20(1): 305-330 (March, 1992). DOI: 10.1214/aos/1176348524


We show that regression quantiles, which could be computed as solutions of a linear programming problem, and the solutions of the corresponding dual problem, which we call the regression rank-scores, generalize the duality of order statistics and of ranks from the location to the linear model. Noting this fact, we study the regression quantile and regression rank-score processes in the heteroscedastic linear regression model, obtaining some new estimators and interesting comparisons with existing estimators.


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C. Gutenbrunner. J. Jureckova. "Regression Rank Scores and Regression Quantiles." Ann. Statist. 20 (1) 305 - 330, March, 1992.


Published: March, 1992
First available in Project Euclid: 12 April 2007

zbMATH: 0759.62015
MathSciNet: MR1150346
Digital Object Identifier: 10.1214/aos/1176348524

Primary: 62G05
Secondary: 62J05

Keywords: $L$-statistic , Linear rank statistic , regression quantile , regression rank-score , trimmed least-squares estimator

Rights: Copyright © 1992 Institute of Mathematical Statistics

Vol.20 • No. 1 • March, 1992
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