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Performance of Some Ridge Regression Estimators for the Multinomial Logit Model
Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics. (Statistik)
Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics. (Statistik)
Department of Mathematics and Statistics, Florida International University, Miami, Florida, USA.
2018 (English)In: Communications in Statistics - Theory and Methods, ISSN 0361-0926, E-ISSN 1532-415X, Vol. 47, no 12, p. 2795-2804Article in journal (Refereed) Published
Abstract [en]

This article considers several estimators for estimating the ridge parameter k for multinomial logit model based on the work of Khalaf and Shukur (2005), Alkhamisi et al. (2006), and Muniz et al. (2012). The mean square error (MSE) is considered as the performance criterion. A simulation study has been conducted to compare the performance of the estimators. Based on the simulation study we found that increasing the correlation between the independent variables and the number of regressors has negative effect on the MSE. However, when the sample size increases the MSE decreases even when the correlation between the independent variables is large. Based on the minimum MSE criterion some useful estimators for estimating the ridge parameter k are recommended for the practitioners

Place, publisher, year, edition, pages
Taylor & Francis, 2018. Vol. 47, no 12, p. 2795-2804
Keywords [en]
Estimation, LSE, MSE, Multicollinearity, Multinomial Logit, Ridge Regression, Simulation, AMS
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:hj:diva-20958DOI: 10.1080/03610926.2013.784996ISI: 000429361000001Scopus ID: 2-s2.0-85045066903Local ID: IHHEFSISOAI: oai:DiVA.org:hj-20958DiVA, id: diva2:616823
Available from: 2013-04-18 Created: 2013-04-18 Last updated: 2018-04-25Bibliographically approved

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Månsson, KristoferShukur, Ghazi

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