Many real-life time series exhibit clusters of outlying observations that cannot be adequately modeled by a Gaussian distribution. Heavy-tailed distributions such as the Pareto distribution have proved useful in modeling a wide range of bursty phenomena that occur in areas as diverse as finance, insurance, telecommunications, meteorology, and hydrology. Regular variation provides a convenient and unified background for studying multivariate extremes when heavy tails are present. In this paper, we study the extreme value behavior of the space–time process given by
where
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is an iid sequence of random fields on
[0,1]d with values in the Skorokhod space
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of càdlàg functions on
41a17ffd81fe9753a60cad51274bb"" title=""Click to view the MathML source"">[0,1]d equipped with the
1a2aeb0ed09fc50a2026114876f977"" title=""Click to view the MathML source"">J1-topology. The coefficients
ψi are deterministic real-valued fields on
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. The indices
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and
t refer to the observation of the process at location
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and time
t. For example,
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, could represent the time series of annual maxima of ozone levels at location
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. The problem of interest is determining the probability that the maximum ozone level over the entire region
[0,1]2 does not exceed a given standard level
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in
n years. By establishing a limit theory for point processes based on
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,
t=1,…,n, we are able to provide approximations for probabilities of extremal events. This theory builds on earlier results of de Haan and Lin [L. de Haan, T. Lin, On convergence toward an extreme value distribution in
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, Ann. Probab. 29 (2001) 467–483] and Hult and Lindskog [H. Hult, F. Lindskog, Extremal behavior of regularly varying stochastic processes, Stochastic Process. Appl. 115 (2) (2005) 249–274] for regular variation on
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and Davis and Resnick [R.A. Davis, S.I. Resnick, Limit theory for moving averages of random variables with regularly varying tail probabilities, Ann. Probab. 13 (1985) 179–195] for extremes of linear processes with heavy-tailed noise.