On the likelihood function of Gaussian max-stable processes

Marc G. Genton; Yanyuan Ma; Huiyan Sang

doi:10.1093/biomet/asr020

On the likelihood function of Gaussian max-stable processes

Marc G. Genton, Yanyuan Ma, Huiyan Sang

Statistics

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76 Scopus citations

Abstract

We derive a closed form expression for the likelihood function of a Gaussian max-stable process indexed by ℝ^d at p≤d+1 sites, d≥1. We demonstrate the gain in efficiency in the maximum composite likelihood estimators of the covariance matrix from p=2 to p=3 sites in ℝ² by means of a Monte Carlo simulation study.

Original language	English (US)
Pages (from-to)	481-488
Number of pages	8
Journal	Biometrika
Volume	98
Issue number	2
DOIs	https://doi.org/10.1093/biomet/asr020
State	Published - Jun 2011

All Science Journal Classification (ASJC) codes

Statistics and Probability
General Mathematics
Agricultural and Biological Sciences (miscellaneous)
General Agricultural and Biological Sciences
Statistics, Probability and Uncertainty
Applied Mathematics

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10.1093/biomet/asr020

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abstract = "We derive a closed form expression for the likelihood function of a Gaussian max-stable process indexed by ℝd at p≤d+1 sites, d≥1. We demonstrate the gain in efficiency in the maximum composite likelihood estimators of the covariance matrix from p=2 to p=3 sites in ℝ2 by means of a Monte Carlo simulation study.",

author = "Genton, \{Marc G.\} and Yanyuan Ma and Huiyan Sang",

note = "Funding Information: This research was sponsored by the U.S. National Science Foundation and by an award made by King Abdullah University of Science and Technology. The authors thank the editor and two referees for very useful comments.",

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N2 - We derive a closed form expression for the likelihood function of a Gaussian max-stable process indexed by ℝd at p≤d+1 sites, d≥1. We demonstrate the gain in efficiency in the maximum composite likelihood estimators of the covariance matrix from p=2 to p=3 sites in ℝ2 by means of a Monte Carlo simulation study.

AB - We derive a closed form expression for the likelihood function of a Gaussian max-stable process indexed by ℝd at p≤d+1 sites, d≥1. We demonstrate the gain in efficiency in the maximum composite likelihood estimators of the covariance matrix from p=2 to p=3 sites in ℝ2 by means of a Monte Carlo simulation study.

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On the likelihood function of Gaussian max-stable processes

Abstract

All Science Journal Classification (ASJC) codes

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