Volume 44, Issue 1
Original Article

Models for Extremal Dependence Derived from Skew‐symmetric Families

Boris Beranger

School of Mathematics and Statistics, University of New South Wales

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Simone A. Padoan

Corresponding Author

E-mail address: simone.padoan@unibocconi.it

Department of Decision Sciences, Bocconi University

Simone A. Padoan, Department of Decision Sciences, Bocconi University, via Roentgen, 1, 20136, Milan, Italy

E‐mail: simone.padoan@unibocconi.it

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Scott A. Sisson

School of Mathematics and Statistics, University of New South Wales

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First published: 13 September 2016
Citations: 4

Abstract

Skew‐symmetric families of distributions such as the skew‐normal and skew‐t represent supersets of the normal and t distributions, and they exhibit richer classes of extremal behaviour. By defining a non‐stationary skew‐normal process, which allows the easy handling of positive definite, non‐stationary covariance functions, we derive a new family of max‐stable processes – the extremal skew‐t process. This process is a superset of non‐stationary processes that include the stationary extremal‐t processes. We provide the spectral representation and the resulting angular densities of the extremal skew‐t process and illustrate its practical implementation.

Number of times cited according to CrossRef: 4

  • High-dimensional inference using the extremal skew-t process, Extremes, 10.1007/s10687-020-00376-1, (2020).
  • Composite likelihood methods for histogram-valued random variables, Statistics and Computing, 10.1007/s11222-020-09955-5, (2020).
  • A Non‐Gaussian Spatio‐Temporal Model for Daily Wind Speeds Based on a Multi‐Variate Skew‐t Distribution, Journal of Time Series Analysis, 10.1111/jtsa.12437, 40, 3, (312-326), (2018).
  • Extremal properties of the multivariate extended skew-normal distribution, Statistics & Probability Letters, 10.1016/j.spl.2018.11.031, (2018).

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