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Main Authors: Bai, Shuyang, Kulik, Rafał, Wang, Yizao
Format: Preprint
Published: 2024
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Online Access:https://arxiv.org/abs/2409.17966
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author Bai, Shuyang
Kulik, Rafał
Wang, Yizao
author_facet Bai, Shuyang
Kulik, Rafał
Wang, Yizao
contents The stable-regenerative multiple-stable model has been shown recently to have distinct candidate extremal index and extremal index. To understand further this rare phenomenon, two more results are established here for the double-stable model. The first is the convergence of point processes for the clusters of extremes, enhancing the previous result on the weak convergence of random sup-measures. Most interestingly, the second result reveals a new phase transition at the mesoscopic level when computing the asymptotic exceedance probability over a block, $\mathbb P(\max_{k=1,\dots,d_n} X_k>b_n)$, as $n\to\infty$. Here, the mesoscopic level is referred to the fact that the block size $d_n$ is allowed to grow at the rate $n^ρ$ with $ρ\in[0,1]$, while the threshold $b_n$ is such that $\mathbb P(X_1>b_n)\sim 1/n$. The recently discovered discrepancy between the candidate extremal index and the extremal index is shown to be just a reflection of this phase transition that is prohibited by the anticlustering condition.
format Preprint
id arxiv_https___arxiv_org_abs_2409_17966
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle A remarkable example on clustering of extremes for regularly-varying stochastic processes
Bai, Shuyang
Kulik, Rafał
Wang, Yizao
Probability
The stable-regenerative multiple-stable model has been shown recently to have distinct candidate extremal index and extremal index. To understand further this rare phenomenon, two more results are established here for the double-stable model. The first is the convergence of point processes for the clusters of extremes, enhancing the previous result on the weak convergence of random sup-measures. Most interestingly, the second result reveals a new phase transition at the mesoscopic level when computing the asymptotic exceedance probability over a block, $\mathbb P(\max_{k=1,\dots,d_n} X_k>b_n)$, as $n\to\infty$. Here, the mesoscopic level is referred to the fact that the block size $d_n$ is allowed to grow at the rate $n^ρ$ with $ρ\in[0,1]$, while the threshold $b_n$ is such that $\mathbb P(X_1>b_n)\sim 1/n$. The recently discovered discrepancy between the candidate extremal index and the extremal index is shown to be just a reflection of this phase transition that is prohibited by the anticlustering condition.
title A remarkable example on clustering of extremes for regularly-varying stochastic processes
topic Probability
url https://arxiv.org/abs/2409.17966