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research article

Robust binary regression

Hosseinian, Sahar
•
Morgenthaler, Stephan  
2011
Journal Of Statistical Planning And Inference

Robust procedures increase the reliability of the results of a data analysis. We studied such a robust procedure for binary regression models based on the criterion of least absolute deviation. The resulting estimating equation consists in a simple modification of the familiar maximum likelihood equation. This estimator is easy to compute with existing computational procedures and gives a high degree of protection. (C) 2010 Elsevier B.V. All rights reserved.

  • Details
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Type
research article
DOI
10.1016/j.jspi.2010.11.015
Web of Science ID

WOS:000286960500013

Author(s)
Hosseinian, Sahar
Morgenthaler, Stephan  
Date Issued

2011

Publisher

Elsevier

Published in
Journal Of Statistical Planning And Inference
Volume

141

Start page

1497

End page

1509

Subjects

Robustness

•

Generalized linear model

•

Logistic model

•

Bounded influence function

•

Maximum likelihood

•

Generalized Linear-Models

•

Logistic-Regression

•

Estimator

•

Fits

Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
STAP  
Available on Infoscience
December 16, 2011
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/74394
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