BIRS Workshop Lecture Videos

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BIRS Workshop Lecture Videos

Full Likelihood Inference For Max-Stable Distributions Based on a Stochastic EM Algorithm Huser, Raphael


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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