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Full Likelihood Inference For Max-Stable Distributions Based on a Stochastic EM Algorithm Huser, Raphael
Description
Max-stable distributions are widely used for the modeling of multivariate extreme events, as they arise as natural limits of renormalized componentwise maxima of random vectors. However, when the dimension is large, the number of terms involved in the likelihood function becomes extremely large, making it intractable for classical inference. In practice, composite likelihoods are often used instead, but suffer from a loss in efficiency. In this talk, an alternative approach to perform full likelihood inference based on an EM algorithm is explored, where an additional random partition associated to the occurrence times of maxima is introduced. Treating this partition as a missing observation, the completed likelihood becomes simple and a (stochastic) EM algorithm may be used to maximize the full likelihood. The performance of this novel approach will be illustrated with numerical results based on the logistic model. \[ \[ Joint work with Clement Dombry, Marc Genton and Mathieu Ribatet. \[ \] Reference : Ailliot P., Delyon B., Monbet V., Prevosto M. Dependent time changed processes with applications to nonlinear ocean waves. arXiv:1510.02302,
Item Metadata
Title |
Full Likelihood Inference For Max-Stable Distributions Based on a Stochastic EM Algorithm
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Creator | |
Publisher |
Banff International Research Station for Mathematical Innovation and Discovery
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Date Issued |
2016-06-16T13:05
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Description |
Max-stable distributions are widely used for the modeling of multivariate extreme events, as they arise as natural limits of renormalized componentwise maxima of random vectors. However, when the dimension is large, the number of terms involved in the likelihood function becomes extremely large, making it intractable for classical inference. In practice, composite likelihoods are often used instead, but suffer from a loss in efficiency. In this talk, an alternative approach to perform full likelihood inference based on an EM algorithm is explored, where an additional random partition associated to the occurrence times of maxima is introduced. Treating this partition as a missing observation, the completed likelihood becomes simple and a (stochastic) EM algorithm may be used to maximize the full likelihood. The performance of this novel approach will be illustrated with numerical results based on the logistic model.
\[ \[
Joint work with Clement Dombry, Marc Genton and Mathieu Ribatet.
\[ \]
Reference :
Ailliot P., Delyon B., Monbet V., Prevosto M. Dependent time changed processes with applications to nonlinear ocean waves. arXiv:1510.02302,
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Extent |
57 minutes
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Subject | |
Type | |
File Format |
video/mp4
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Language |
eng
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Notes |
Author affiliation: King Abdullah University of Science and Technology
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Series | |
Date Available |
2016-12-23
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Provider |
Vancouver : University of British Columbia Library
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Rights |
Attribution-NonCommercial-NoDerivatives 4.0 International
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DOI |
10.14288/1.0340435
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URI | |
Affiliation | |
Peer Review Status |
Unreviewed
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Scholarly Level |
Faculty
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Rights URI | |
Aggregated Source Repository |
DSpace
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Item Citations and Data
Rights
Attribution-NonCommercial-NoDerivatives 4.0 International