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