Improved ridge regression estimators for the logistic regression model

A. Saleh and B. Kibria ()

Computational Statistics, 2013, vol. 28, issue 6, 2519-2558

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. Copyright Springer-Verlag Berlin Heidelberg 2013

Keywords: Dominance; Efficiency; Pre-test; Risk function; Stein-rule estimator (search for similar items in EconPapers)
Date: 2013
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Persistent link: https://EconPapers.repec.org/RePEc:spr:compst:v:28:y:2013:i:6:p:2519-2558

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DOI: 10.1007/s00180-013-0417-6

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