Robust regression with high coverage

David J. Olive; Douglas M. Hawkins

doi:10.1016/S0167-7152(03)00090-7

Robust regression with high coverage

David J. Olive, Douglas M. Hawkins

Statistics (Twin Cities)

Research output: Contribution to journal › Article › peer-review

7 Scopus citations

Abstract

An important parameter for several high breakdown regression algorithm estimators is the number of cases given weight one, called the coverage of the estimator. Increasing the coverage is believed to result in a more stable estimator, but the price paid for this stability is greatly decreased resistance to outliers. A simple modification of the algorithm can greatly increase the coverage and hence its statistical performance while maintaining high outlier resistance.

Original language	English (US)
Pages (from-to)	259-266
Number of pages	8
Journal	Statistics and Probability Letters
Volume	63
Issue number	3
DOIs	https://doi.org/10.1016/S0167-7152(03)00090-7
State	Published - Jul 1 2003

Bibliographical note

Funding Information:
This research was supported by NSF grants DMS 9806584 and DMS 0202922. The authors are grateful to the editor and a referee for a number of helpful suggestions for improvement in the article.

Keywords

Elemental sets
LMS
LTA
LTS
Outliers

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10.1016/S0167-7152(03)00090-7

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title = "Robust regression with high coverage",

abstract = "An important parameter for several high breakdown regression algorithm estimators is the number of cases given weight one, called the coverage of the estimator. Increasing the coverage is believed to result in a more stable estimator, but the price paid for this stability is greatly decreased resistance to outliers. A simple modification of the algorithm can greatly increase the coverage and hence its statistical performance while maintaining high outlier resistance.",

keywords = "Elemental sets, LMS, LTA, LTS, Outliers",

author = "Olive, {David J.} and Hawkins, {Douglas M.}",

note = "Funding Information: This research was supported by NSF grants DMS 9806584 and DMS 0202922. The authors are grateful to the editor and a referee for a number of helpful suggestions for improvement in the article. ",

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T1 - Robust regression with high coverage

AU - Olive, David J.

AU - Hawkins, Douglas M.

N1 - Funding Information: This research was supported by NSF grants DMS 9806584 and DMS 0202922. The authors are grateful to the editor and a referee for a number of helpful suggestions for improvement in the article.

PY - 2003/7/1

Y1 - 2003/7/1

N2 - An important parameter for several high breakdown regression algorithm estimators is the number of cases given weight one, called the coverage of the estimator. Increasing the coverage is believed to result in a more stable estimator, but the price paid for this stability is greatly decreased resistance to outliers. A simple modification of the algorithm can greatly increase the coverage and hence its statistical performance while maintaining high outlier resistance.

AB - An important parameter for several high breakdown regression algorithm estimators is the number of cases given weight one, called the coverage of the estimator. Increasing the coverage is believed to result in a more stable estimator, but the price paid for this stability is greatly decreased resistance to outliers. A simple modification of the algorithm can greatly increase the coverage and hence its statistical performance while maintaining high outlier resistance.

KW - Elemental sets

KW - LMS

KW - LTA

KW - LTS

KW - Outliers

UR - http://www.scopus.com/inward/record.url?scp=0037905618&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0037905618&partnerID=8YFLogxK

U2 - 10.1016/S0167-7152(03)00090-7

DO - 10.1016/S0167-7152(03)00090-7

M3 - Article

AN - SCOPUS:0037905618

SN - 0167-7152

VL - 63

SP - 259

EP - 266

JO - Statistics and Probability Letters

JF - Statistics and Probability Letters

IS - 3

ER -

Robust regression with high coverage

Abstract

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Keywords

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