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Performance of Some Logistic Ridge Regression Estimators
Linnaeus University, Faculty of Business, Economics and Design, Linnaeus School of Business and Economics.ORCID iD: 0000-0002-3416-5896
2012 (English)In: Computational Economics, ISSN 0927-7099, E-ISSN 1572-9974, Vol. 40, no 4, 401-414 p.Article in journal (Refereed) Published
Abstract [en]

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).

Place, publisher, year, edition, pages
2012. Vol. 40, no 4, 401-414 p.
National Category
Economics and Business
Research subject
Statistics/Econometrics
Identifiers
URN: urn:nbn:se:lnu:diva-22565DOI: 10.1007/s10614-011-9275-xScopus ID: 2-s2.0-84868539799OAI: oai:DiVA.org:lnu-22565DiVA: diva2:571118
Available from: 2012-11-21 Created: 2012-11-21 Last updated: 2017-12-07Bibliographically approved

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Shukur, Ghazi

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