Extremes of deterministic sub‐sampled moving averages with heavy‐tailed innovations
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.
Citing Literature
Number of times cited according to CrossRef: 5
- Christopher S. Withers, Saralees Nadarajah, 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).
- M. Ivette Gomes, Andreia Hall, M. Cristina Miranda, 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).
- M. Scotto, 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).
- A. Hall, M. G. Scotto, H. Ferreira, 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).
- A.P Martins, H Ferreira, 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).




