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A generic algorithm for reducing bias in parametric estimation

By Ioannis Kosmidis and David Firth

Abstract

A general iterative algorithm is developed for the computation\ud of reduced-bias parameter estimates in regular statistical models through\ud adjustments to the score function. The algorithm unifies and provides appealing new interpretation for iterative methods that have been published\ud previously for some specific model classes. The new algorithm can usefully be viewed as a series of iterative bias corrections, thus facilitating the\ud adjusted score approach to bias reduction in any model for which the first-\ud order bias of the maximum likelihood estimator has already been derived.\ud The method is tested by application to a logit-linear multiple regression\ud model with beta-distributed responses; the results confirm the effectiveness\ud of the new algorithm, and also reveal some important errors in the existing\ud literature on beta regression

Topics: QA
Publisher: Institute of Mathematical Statistics
Year: 2010
OAI identifier: oai:wrap.warwick.ac.uk:4341

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