'Bias reduction of maximum likelihood estimates'
Copyright policy )'Bias reduction of maximum likelihood estimates'
Summary: 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.
- University of Southampton United Kingdom
Generalized linear models (logistic models), Jeffreys invariant prior, logistic regression, Point estimation, first-order term, iterative adjustments, canonical parameterization, generalized linear models, Jeffreys prior penalty function, Poisson log linear models, shrinkage, exponential families, biased estimating equations, asymptotic bias, asymptotic bias of maximum likelihood estimates, score function, penalized likelihood, binomial logistic models, modified score
Generalized linear models (logistic models), Jeffreys invariant prior, logistic regression, Point estimation, first-order term, iterative adjustments, canonical parameterization, generalized linear models, Jeffreys prior penalty function, Poisson log linear models, shrinkage, exponential families, biased estimating equations, asymptotic bias, asymptotic bias of maximum likelihood estimates, score function, penalized likelihood, binomial logistic models, modified score
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