A generic algorithm for reducing bias in parametric estimation.
ELECTRON J STAT
1097 - 1112.
A general iterative algorithm is developed for the computation of reduced-bias parameter estimates in regular statistical models through adjustments to the score function. 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 use fully 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.
|Title:||A generic algorithm for reducing bias in parametric estimation|
|Open access status:||An open access publication|
|Keywords:||Adjusted score, asymptotic bias correction, beta regression, bias reduction, fisher scoring, prater gasoline data, LINEAR MIXED MODELS, LOGISTIC-REGRESSION, BETA-REGRESSION, NONLINEAR-REGRESSION, LIKELIHOOD, DISPERSION, SEPARATION, REDUCTION|
|UCL classification:||UCL > School of BEAMS > Faculty of Maths and Physical Sciences > Statistical Science|
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