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On Ridge Parameters in Logistic Regression
Jönköping University, Jönköping International Business School, JIBS, Economics. Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics. (Statistik)
Jönköping University, Jönköping International Business School, JIBS, Economics. Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics. (Statistik)
2011 (English)In: Communications in Statistics - Theory and Methods, ISSN 0361-0926, E-ISSN 1532-415X, Vol. 40, no 18, 3366-3381 p.Article in journal (Refereed) Published
Abstract [en]

This article applies and investigates a number of logistic ridge regression (RR) parameters that are estimable by using the maximum likelihood (ML) method. By conducting an extensive Monte Carlo study, the performances of ML and logistic RR are investigated in the presence of multicollinearity and under different conditions. The simulation study evaluates a number of methods of estimating the RR parameter k that has recently been developed for use in linear regression analysis. The results from the simulation study show that there is at least one RR estimator that has a lower mean squared error (MSE) than the ML method for all the different evaluated situations.

Place, publisher, year, edition, pages
2011. Vol. 40, no 18, 3366-3381 p.
Keyword [en]
Logistic regression, Maximum likelihood, Monte Carlo simulations, MSE, Multicollinearity, Ridge regression
National Category
Economics
Identifiers
URN: urn:nbn:se:hj:diva-13667DOI: 10.1080/03610926.2010.500111OAI: oai:DiVA.org:hj-13667DiVA: diva2:359821
Available from: 2010-10-30 Created: 2010-10-30 Last updated: 2013-01-18Bibliographically approved

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