Kosmidis, Ioannis and Firth, David (2010) A generic algorithm for reducing bias in parametric estimation. Electronic Journal of Statistics, Vol.4 . pp. 1097-1112. doi:10.1214/10-EJS579 ISSN 1935-7524.

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Abstract

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 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.

Item Type:	Journal Article
Subjects:	Q Science > QA Mathematics
Divisions:	Faculty of Science, Engineering and Medicine > Science > Statistics
Library of Congress Subject Headings (LCSH):	Parameter estimation, Mathematical models
Journal or Publication Title:	Electronic Journal of Statistics
Publisher:	Institute of Mathematical Statistics
ISSN:	1935-7524
Official Date:	2010
Dates:	Date Event 2010 Published
Volume:	Vol.4
Page Range:	pp. 1097-1112
DOI:	10.1214/10-EJS579
Status:	Peer Reviewed
Access rights to Published version:	Open Access (Creative Commons)
Funder:	Engineering and Physical Sciences Research Council (EPSRC)

Data sourced from Thomson Reuters' Web of Knowledge

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