Miasojedow, B, Niemiro, W, Miasojedow, B et al. (2 more authors) (2015) Adaptive monte carlo maximum likelihood. In: Matwin, S and Mielniczuk, J, (eds.) Challenges in Computational Statistics and Data Mining. Studies in Computational Intelligence, 605 . Springer , pp. 247-270. ISBN 978-3-319-18780-8
Abstract
We consider Monte Carlo approximations to the maximum likelihood estimator in models with intractable norming constants. This paper deals with adaptive Monte Carlo algorithms, which adjust control parameters in the course of simulation. We examine asymptotics of adaptive importance sampling and a new algorithm, which uses resampling and MCMC. This algorithm is designed to reduce problems with degeneracy of importance weights. Our analysis is based on martingale limit theorems. We also describe how adaptive maximization algorithms of Newton-Raphson type can be combined with the resampling techniques. The paper includes results of a small scale simulation study in which we compare the performance of adaptive and non-adaptive Monte Carlo maximum likelihood algorithms.
Metadata
Item Type: | Book Section |
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Authors/Creators: |
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Editors: |
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Keywords: | Maximum likelihood; Importance sampling; Adaptation; MCMC; Resampling |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mathematics (Leeds) > Statistics (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 24 Nov 2016 13:51 |
Last Modified: | 24 Nov 2016 13:51 |
Published Version: | http://dx.doi.org/10.1007/978-3-319-18781-5_14 |
Status: | Published |
Publisher: | Springer |
Series Name: | Studies in Computational Intelligence |
Identification Number: | 10.1007/978-3-319-18781-5_14 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:88925 |