Volume 19, Issue 4
Research Article

Extremes of deterministic sub‐sampled moving averages with heavy‐tailed innovations

M. Scotto

E-mail address: mscotto@mat.ua.pt

Department of Mathematics, University of Aveiro, Portugal

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H. Ferreira

Corresponding Author

E-mail address: ferreira@fenix2.ubi.pt

Department of Mathematics, University of Beira Interior, Portugal

Department of Mathematics, University of Aveiro, Campo Universitário de Santiago, 3810‐193 Aveiro, PortugalSearch for more papers by this author
First published: 12 November 2003
Citations: 5

Abstract

Let {Xk}k⩾1 be a strictly stationary time series. For a strictly increasing sampling function g:ℕ→ℕ define Yk=Xg(k) as the deterministic sub‐sampled time series. In this paper, the extreme value theory of {Yk} is studied when Xk has representation as a moving average driven by heavy‐tailed innovations. Under mild conditions, convergence results for a sequence of point processes based on {Yk} are proved and extremal properties of the deterministic sub‐sampled time series are derived. In particular, we obtain the limiting distribution of the maximum and the corresponding extremal index. Copyright © 2003 John Wiley & Sons, Ltd.

Number of times cited according to CrossRef: 5

  • The distribution of the maximum of a first order moving average: the continuous case, Extremes, 10.1007/s10687-013-0172-7, 17, 1, (1-24), (2013).
  • Subsampling techniques and the Jackknife methodology in the estimation of the extremal index, Computational Statistics & Data Analysis, 10.1016/j.csda.2007.06.023, 52, 4, (2022-2041), (2008).
  • Extremes of a class of deterministic sub-sampled processes with applications to stochastic difference equations, Stochastic Processes and their Applications, 10.1016/j.spa.2004.09.009, 115, 3, (417-434), (2005).
  • On the Extremal Behaviour of Generalised Periodic Sub-Sampled Moving Average Models with Regularly Varying Tails, Extremes, 10.1007/s10687-005-6197-9, 7, 2, (149-160), (2005).
  • The extremal index of sub-sampled processes, Journal of Statistical Planning and Inference, 10.1016/S0378-3758(03)00194-0, 124, 1, (145-152), (2004).

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