Kosmidis, Ioannis (2014) Bias in parametric estimation : reduction and useful side-effects. Wiley Interdisciplinary Reviews: Computational Statistics, 6 (3). pp. 185-196.

Preview	PDF WRAP-bias-parametric-estimation-reduction-useful-side-effects-Kosmidis-2014.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution Non-commercial. Download (507Kb) | Preview
	PDF 1311.6311.pdf Embargoed item. Restricted access to Repository staff only - Requires a PDF viewer. Download (209Kb)

Request Changes to record.

Abstract

The bias of an estimator is defined as the difference of its expected value from the parameter to be estimated, where the expectation is with respect to the model. Loosely speaking, small bias reflects the desire that if an experiment is repeated indefinitely then the average of all the resultant estimates will be close to the parameter value that is estimated. The current article is a review of the still-expanding repository of methods that have been developed to reduce bias in the estimation of parametric models. The review provides a unifying framework where all those methods are seen as attempts to approximate the solution of a simple estimating equation. Of particular focus is the maximum likelihood estimator, which despite being asymptotically unbiased under the usual regularity conditions, has finite-sample bias that can result in significant loss of performance of standard inferential procedures. An informal comparison of the methods is made revealing some useful practical side-effects in the estimation of popular models in practice including: (1) shrinkage of the estimators in binomial and multinomial regression models that guarantees finiteness even in cases of data separation where the maximum likelihood estimator is infinite and (2) inferential benefits for models that require the estimation of dispersion or precision parameters.

Item Type:	Journal Article
Subjects:	H Social Sciences > HA Statistics Q Science > QA Mathematics
Divisions:	Faculty of Science > Statistics
Library of Congress Subject Headings (LCSH):	Mathematical statistics, Estimation theory, Jackknife (Statistics), Bootstrap (Statistics)
Journal or Publication Title:	Wiley Interdisciplinary Reviews: Computational Statistics
Publisher:	John Wiley & Sons, Inc.
ISSN:	0006-3444
Official Date:	May 2014
Dates:	Date Event May 2014 Published 25 March 2014 Available 4 February 2014 Accepted
Date of first compliant deposit:	15 February 2018
Volume:	6
Number:	3
Page Range:	pp. 185-196
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Open Access

Request changes or add full text files to a record

Repository staff actions (login required)

View Item