Performance of Some Logistic Ridge Regression Estimators

B. Kibria (), Kristofer Månsson () and Ghazi Shukur

Computational Economics, 2012, vol. 40, issue 4, 414 pages

Abstract: In this paper we generalize different approaches of estimating the ridge parameter k proposed by Muniz et al. (Comput Stat, 2011 ) to be applicable for logistic ridge regression (LRR). These new methods of estimating the ridge parameter in LRR are evaluated by means of Monte Carlo simulations along with the some other estimators of k that has already been evaluated by Månsson and Shukur (Commun Stat Theory Methods, 2010 ) together with the traditional maximum likelihood (ML) approach. As a performance criterion we use the mean squared error (MSE). In the simulation study we also calculate the mean value and the standard deviation of k. The average value is interesting firstly in order to see what values of k that are reasonable and secondly if several estimators have equal variance then the estimator that induces the smallest bias should be chosen. The standard deviation is interesting as a performance criteria if several estimators of k have the same MSE, then the most stable estimator (with the lowest standard deviation) should be chosen. The result from the simulation study shows that LRR outperforms ML approach. Furthermore, some of new proposed ridge estimators outperformed those proposed by Månsson and Shukur (Commun Stat Theory Methods, 2010 ). Copyright Springer Science+Business Media, LLC. 2012

Keywords: Estimation; Logit; MSE; Multicollinearity; Ridge regression; Simulation; Primary 62J07; Secondary 62F1Q (search for similar items in EconPapers)
Date: 2012
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Citations: View citations in EconPapers (4)

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DOI: 10.1007/s10614-011-9275-x

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