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Multinomial logit bias reduction via the Poisson log-linear model

Kosmidis, I; Firth, D; (2011) Multinomial logit bias reduction via the Poisson log-linear model. BIOMETRIKA , 98 (3) 755 - 759. 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.

Type: Article
Title: Multinomial logit bias reduction via the Poisson log-linear model
DOI: 10.1093/biomet/asr026
Keywords: Jeffreys prior, Leverage, Logistic linear regression, Poisson trick, LOGISTIC-REGRESSION, MAXIMUM-LIKELIHOOD
UCL classification: UCL > School of BEAMS > Faculty of Maths and Physical Sciences > Statistical Science
URI: http://discovery.ucl.ac.uk/id/eprint/1323654
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