The least trimmed quantile regression

M.N. Neykov; P. Cizek; P. Filzmoser; P.N. Neytchev

doi:10.1016/j.csda.2011.10.023

The least trimmed quantile regression

M.N. Neykov, P. Cizek, P. Filzmoser, P.N. Neytchev

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18 Citations (Scopus)

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.

Original language	English
Pages (from-to)	1757-1770
Journal	Computational Statistics & Data Analysis
Volume	56
Issue number	6
DOIs	https://doi.org/10.1016/j.csda.2011.10.023
Publication status	Published - 2012

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10.1016/j.csda.2011.10.023

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title = "The least trimmed quantile regression",

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.",

author = "M.N. Neykov and P. Cizek and P. Filzmoser and P.N. Neytchev",

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AU - Neykov, M.N.

AU - Cizek, P.

AU - Filzmoser, P.

AU - Neytchev, P.N.

PY - 2012

Y1 - 2012

N2 - 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.

AB - 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.

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DO - 10.1016/j.csda.2011.10.023

M3 - Article

SN - 0167-9473

VL - 56

SP - 1757

EP - 1770

JO - Computational Statistics & Data Analysis

JF - Computational Statistics & Data Analysis

IS - 6

ER -

The least trimmed quantile regression

Abstract

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