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Multinomial logit bias reduction via Poisson log-linear model

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Kosmidis, Ioannis and Firth, David (2011) Multinomial logit bias reduction via Poisson log-linear model. Biometrika, 98 (3). pp. 755-759. doi:10.1093/biomet/asr026

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Official URL: http://dx.doi.org/10.1093/biomet/asr026

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Abstract

For the parameters of a multinomial logistic regression, it is shown how to obtain the bias-reducing penalized maximum likelihood estimator by using the equivalent Poisson log-linear model. The calculation needed is not simply an application of the Jeffreys prior penalty to the Poisson model. The development allows a simple and computationally efficient implementation of the reduced-bias estimator, using standard software for generalized linear models.

Item Type: Journal Article
Subjects: Q Science > QA Mathematics
Divisions: Faculty of Science, Engineering and Medicine > Science > Statistics
Library of Congress Subject Headings (LCSH): Parameter estimation
Series Name: Working papers
Journal or Publication Title: Biometrika
Publisher: Biometrika Trust
Place of Publication: Coventry
ISSN: 0006-3444
Official Date: September 2011
Dates:
DateEvent
September 2011Published
13 July 2011Accepted
Volume: 98
Number: 3
Number of Pages: 4
Page Range: pp. 755-759
DOI: 10.1093/biomet/asr026
Status: Not Peer Reviewed
Publication Status: Published
Access rights to Published version: Restricted or Subscription Access
Funder: Engineering and Physical Sciences Research Council (EPSRC)
Version or Related Resource: Kosmidis, I. and Firth, D. (2010). Multinomial logit bias reduction via Poisson log-linear model. [Coventry] : University of Warwick. Centre for Research in Statistical Methodology. (Working papers, no.10-18).
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  • Multinomial logit bias reduction via Poisson log-linear model. (deposited 13 Jun 2011 15:55)
    • Multinomial logit bias reduction via Poisson log-linear model. (deposited 12 Oct 2011 16:22) [Currently Displayed]

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