JSM 2011 Online Program

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Abstract Details

Activity Number: 304
Type: Contributed
Date/Time: Tuesday, August 2, 2011 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistical Computing
Abstract - #302865
Title: A Generic Algorithm for Reducing Bias in Parametric Estimation
Author(s): Ioannis Kosmidis*+
Companies: University College London
Address: Department of Statistical Science, London, International, WC1E 6BT, United Kingdom
Keywords: Adjusted score ; asymptotic bias correction ; bias reduction ; fisher scoring ; beta regression

A general iterative algorithm is developed for the computation of reduced-bias parameter estimates in regular statistical models through adjustments to the score function (Kosmidis & Firth, 2010, Electronic Journal of Statistics). The algorithm unifies and provides appealing new interpretation for iterative methods that have been published previously for some specific model classes. The new algorithm can usefully be viewed as a series of iterative bias corrections, thus facilitating the adjusted score approach to bias reduction in any model for which the first-order bias of the maximum likelihood estimator has already been derived. The method is tested by application to a logit-linear multiple regression model with beta-distributed responses; the results confirm the effectiveness of the new algorithm, and also reveal some important errors in the existing literature on beta regression.

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