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arXiv · 2011.14747

Asymptotic bias reduction of maximum likelihood estimates via penalized likelihoods with differential geometry

Masayo Y. Hirose·Shuhei Mano
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Abstract

A procedure for asymptotic bias reduction of maximum likelihood estimates of generic estimands is developed. The estimator is realized as a plug-in estimator, where the parameter maximizes the penalized likelihood with a penalty function that satisfies a quasi-linear partial differential equation of the first order. The integration of the partial differential equation with the aid of differential geometry is discussed. Applications to generalized linear models, linear mixed-effects models, and a location-scale family are presented.

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Masayo Y. Hirose, Shuhei Mano. 2020-11-30. Asymptotic bias reduction of maximum likelihood estimates via penalized likelihoods with differential geometry. https://arxiv.org/abs/2011.14747

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