Robust regression quantiles

The regression quantile estimate introduced by Koenker and Bassett in 1978 may not be robust when the predictors contain leverage points. We define estimates which are free of this drawback, and furthermore attain the maximum breakdown point for this problem. Simulations show them to behave generall...

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Bibliographic Details
Published inJournal of statistical planning and inference Vol. 122; no. 1; pp. 187 - 202
Main Authors Adrover, Jorge, Maronna, Ricardo A., Yohai, Víctor J.
Format Journal Article Conference Proceeding
LanguageEnglish
Published Lausanne Elsevier B.V 01.05.2004
New York,NY Elsevier Science
Amsterdam
Subjects
Breakdown point
Exact sciences and technology
Linear inference, regression
Mathematics
Probability and statistics
Projection estimates
Regression depth
Robust regression
Sciences and techniques of general use
Statistics
Regression depth
Projection estimates
primary 62F35
Robust regression
secondary 62J05
Breakdown point
Statistical method
Statistical regression
Statistical decision
Quantile
Estimator robustness
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Summary:The regression quantile estimate introduced by Koenker and Bassett in 1978 may not be robust when the predictors contain leverage points. We define estimates which are free of this drawback, and furthermore attain the maximum breakdown point for this problem. Simulations show them to behave generally better than competing robust quantile estimates.
ISSN:0378-3758
1873-1171
DOI:10.1016/j.jspi.2003.06.009