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/).
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 -