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- Title
Improved ridge regression estimators for the logistic regression model.
- Authors
Saleh, A. K. Md. E.; Kibria, B. M. Golam
- Abstract
The estimation of the regression parameters for the ill-conditioned logistic regression model is considered in this paper. We proposed five ridge regression (RR) estimators, namely, unrestricted RR, restricted ridge regression, preliminary test RR, shrinkage ridge regression and positive rule RR estimators for estimating the parameters $$(\beta )$$ when it is suspected that the parameter $$\beta $$ may belong to a linear subspace defined by $$H\beta =h$$ . Asymptotic properties of the estimators are studied with respect to quadratic risks. The performances of the proposed estimators are compared based on the quadratic bias and risk functions under both null and alternative hypotheses, which specify certain restrictions on the regression parameters. The conditions of superiority of the proposed estimators for departure and ridge parameters are given. Some graphical representations and efficiency analysis have been presented which support the findings of the paper.
- Subjects
RIDGE regression (Statistics); LOGISTIC regression analysis; PARAMETERS (Statistics); PRE-tests &; post-tests; HYPOTHESIS; STATISTICAL research
- Publication
Computational Statistics, 2013, Vol 28, Issue 6, p2519
- ISSN
0943-4062
- Publication type
Article
- DOI
10.1007/s00180-013-0417-6