Proceedings of the 2015 International Conference on Social Science, Education Management and Sports Education

On the almost unbiased Ridge and Liu estimator in the Logistic regression model

Authors
Xinfeng Chang
Corresponding Author
Xinfeng Chang
Available Online November 2015.
DOI
10.2991/ssemse-15.2015.424How to use a DOI?
Keywords
Logistic regression; Maximum likelihood estimator; Ridge regression estimator
Abstract

This paper is concerned with the parameter estimation in logistic regression model. To overcome the multicollinearity problem, Schaefer et al. (1984) and Urgan and Tez (2008), respectively, proposed the logistic ridge regression estimator and logistic Liu estimator for the logistic regression model. In this article, the almost unbiased Ridge and Liu estimator are proposed by applying the almost unbiased method. Necessary and sufficient conditions for the superiority of the new estimators over the logistic ridge regression estimator and logistic Liu estimator in the mean squared error sense are derived.

Copyright
© 2015, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2015 International Conference on Social Science, Education Management and Sports Education
Series
Advances in Social Science, Education and Humanities Research
Publication Date
November 2015
ISBN
10.2991/ssemse-15.2015.424
ISSN
2352-5398
DOI
10.2991/ssemse-15.2015.424How to use a DOI?
Copyright
© 2015, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Xinfeng Chang
PY  - 2015/11
DA  - 2015/11
TI  - On the almost unbiased Ridge and Liu estimator in the Logistic regression model
BT  - Proceedings of the 2015 International Conference on Social Science, Education Management and Sports Education
PB  - Atlantis Press
SP  - 1658
EP  - 1660
SN  - 2352-5398
UR  - https://doi.org/10.2991/ssemse-15.2015.424
DO  - 10.2991/ssemse-15.2015.424
ID  - Chang2015/11
ER  -