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A simple approach to maximum intractable likelihood estimation

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Rubio, Francisco J. and Johansen, Adam M. (2013) A simple approach to maximum intractable likelihood estimation. Electronic Journal of Statistics, Volume 7 . pp. 1632-1654. doi:10.1214/13-EJS819 ISSN 1935-7524.

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Official URL: http://dx.doi.org/10.1214/13-EJS819

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Abstract

Approximate Bayesian Computation (ABC) can be viewed as an analytic approximation of an intractable likelihood coupled with an elementary simulation step. Such a view, combined with a suitable instrumental prior distribution permits maximum-likelihood (or maximum-a-posteriori) inference to be conducted, approximately, using essentially the same techniques. An elementary approach to this problem which simply obtains a nonparametric approximation of the likelihood surface which is then maximised is developed here and the convergence of this class of algorithms is characterised theoretically. The use of non-sufficient summary statistics in this context is considered. Applying the proposed method to four problems demonstrates good performance. The proposed approach provides an alternative for approximating the maximum likelihood estimator (MLE) in complex scenarios.

Item Type: Journal Article
Subjects: Q Science > QA Mathematics
Divisions: Faculty of Science, Engineering and Medicine > Science > Statistics
Library of Congress Subject Headings (LCSH): Bayesian statistical decision theory, Estimation theory
Journal or Publication Title: Electronic Journal of Statistics
Publisher: Institute of Mathematical Statistics
ISSN: 1935-7524
Official Date: 19 June 2013
Dates:
DateEvent
19 June 2013Published
Volume: Volume 7
Number of Pages: 22
Page Range: pp. 1632-1654
DOI: 10.1214/13-EJS819
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Open Access (Creative Commons)
Date of first compliant deposit: 28 December 2015
Date of first compliant Open Access: 28 December 2015
Funder: Consejo Nacional de Ciencia y Tecnología (Mexico) [Mexican Council for Science and Technology] (CONACYT), Engineering and Physical Sciences Research Council (EPSRC)
Grant number: EP/I017984/1 (EPSRC)

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