A modified score function estimator for multinomial logistic regression in small samples

By S. Bull, Carmen Mak, C. Greenwood. 2002

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Jeffreys-prior penalty, finiteness and shrinkage in binomial-response generalized linear models (2018, background): “A different extension, to multinomial models (Bull et al., 2002), has also had influence in various applied fields.”
Multinomial logit bias reduction via the Poisson log-linear model (2011, methodology): “…penalty to remove the O(n−1) asymptotic bias of the maximum likelihood estimator in full exponential family models was developed in Firth (1993) and has been found to be particularly effective in binomial and multinomial logistic regressions (e.g., Heinze & Schemper, 2002; Bull et al., 2002, 2007).”
Bias reduction in exponential family nonlinear models (2009, unknown): “Corresponding empirical studies in Bull et al. (2002) extended these remarks to the case of multinomial-response logistic regression. They compared the biasreduction method with other bias correction methods and accorded it a preferred position amongst them, again in terms of the properties of the r”