Bias reduction of maximum likelihood estimates

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ID: 289723
1993
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Abstract
It is shown how, in regular parametric problems, the first-order term is removed from the asymptotic bias of maximum likelihood estimates by a suitable modification of the score function. In exponential families with canonical parameterization the effect is to penalize the likelihood by the Jeffreys invariant prior. In binomial logistic models, Poisson log linear models and certain other generalized linear models, the Jeffreys prior penalty function can be imposed in standard regression software using a scheme of iterative adjustments to the data.
Reference Key
openalex_W2078129874 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors David Firth
Journal jurnal biometrika dan kependudukan
Year 1993
DOI
10.1093/biomet/80.1.27
URL
https://doi.org/10.1093/biomet/80.1.27
Keywords Keywords not found

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