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A new kind of stochastic restricted biased estimator for logistic regression model
Department of Mathematics, College of Education for Pure Sciences, University of Anbar, Ramadi, Iraq.
Jönköping University, Jönköping International Business School, JIBS, Statistics.ORCID iD: 0000-0002-4535-3630
Department of Mathematics and Statistics, Florida International University, Miami, FL, United States.
2021 (English)In: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532, Vol. 48, no 9, p. 1559-1578Article in journal (Refereed) Published
Abstract [en]

In the logistic regression model, the variance of the maximum likelihood estimator is inflated and unstable when the multicollinearity exists in the data. There are several methods available in literature to overcome this problem. We propose a new stochastic restricted biased estimator. We study the statistical properties of the proposed estimator and compare its performance with some existing estimators in the sense of scalar mean squared criterion. An example and a simulation study are provided to illustrate the performance of the proposed estimator. 

Place, publisher, year, edition, pages
Taylor & Francis, 2021. Vol. 48, no 9, p. 1559-1578
Keywords [en]
Logistic regression, maximum likelihood estimator, mean squared error matrix, ridge regression, simulation study, stochastic restricted estimator
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:hj:diva-50096DOI: 10.1080/02664763.2020.1769576ISI: 000541982900001Scopus ID: 2-s2.0-85086676552Local ID: HOA;intsam;1454545OAI: oai:DiVA.org:hj-50096DiVA, id: diva2:1454545
Available from: 2020-07-17 Created: 2020-07-17 Last updated: 2021-12-06Bibliographically approved

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Månsson, Kristofer

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