Aalto, Juha Antero

Cooley, DS

Davison, AC

Heikkinen, Juha

Thibaud, Emeric

arXiv

Aalto, Juha

Cooley, Daniel S.

Davison, Anthony C.

Jukuri

Bayesian inference for the Brown-Resnick process, with an application to extreme low temperatures

The Brown-Resnick max-stable process has proven to be well suited for modeling extremes of complex environmental processes, but in many applications its likelihood function is intractable and inference must be based on a composite likelihood, thereby preventing the use of classical Bayesian techniques. In this paper we exploit a case in which the full likelihood of a Brown-Resnick process can be calculated, using componentwise maxima and their partitions in terms of individual events, and we propose two new approaches to inference. The first estimates the partitions using declustering, while the second uses random partitions in a Markov chain Monte Carlo algorithm. We use these approaches to construct a Bayesian hierarchical model for extreme low temperatures in northern Fennoscandia.STA

Infoscience - École polytechnique fédérale de Lausanne

Bayesian Inference For The Brown-Resnick Process, With An Application To Extreme Low Temperatures

Emeric Thibaud

Juha Aalto

Daniel S. Cooley

Anthony C. Davison

Juha Heikkinen

Crossref

The Annals of Applied Statistics

Bayesian inference for the Brown–Resnick process, with an application to extreme low temperatures

Helsingin yliopiston digitaalinen arkisto

Bayesian inference for the Brown-Resnick process, with an application to extreme low temperatures

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Jukuri

Jukuri

Infoscience - École polytechnique fédérale de Lausanne

Crossref

Helsingin yliopiston digitaalinen arkisto