The least trimmed quantile regression

N.M. Neykov, Pavel Cizek, Peter Filzmoser and P.N. Neytchev

Computational Statistics & Data Analysis, 2012, vol. 56, issue 6, 1757-1770

Abstract: The linear quantile regression estimator is very popular and widely used. It is also well known that this estimator can be very sensitive to outliers in the explanatory variables. In order to overcome this disadvantage, the usage of the least trimmed quantile regression estimator is proposed to estimate the unknown parameters in a robust way. As a prominent measure of robustness, the breakdown point of this estimator is characterized and its consistency is proved. The performance of this approach in comparison with the classical one is illustrated by an example and simulation studies.

Keywords: Linear regression; Quantile regression; Least trimmed quantile regression; Breakdown point; Outlier detection (search for similar items in EconPapers)
Date: 2012
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Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:56:y:2012:i:6:p:1757-1770

DOI: 10.1016/j.csda.2011.10.023

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