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

Fuente: arXiv
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Main Authors: Hirose, Masayo Y., Mano, Shuhei
Format: Preprint
Published: 2020
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_version_ 1866914726589497344
author Hirose, Masayo Y.
Mano, Shuhei
author_facet Hirose, Masayo Y.
Mano, Shuhei
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2011_14747
institution arXiv
publishDate 2020
record_format arxiv
spellingShingle Asymptotic bias reduction of maximum likelihood estimates via penalized likelihoods with differential geometry
Hirose, Masayo Y.
Mano, Shuhei
Statistics Theory
62F12, 62B11, 62H12
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.
title Asymptotic bias reduction of maximum likelihood estimates via penalized likelihoods with differential geometry
topic Statistics Theory
62F12, 62B11, 62H12
url https://arxiv.org/abs/2011.14747