Phase transition for extremes of a stochastic model with long-range dependence and multiplicative noise

Olivier Durieu and Yizao Wang

Stochastic Processes and their Applications, 2022, vol. 143, issue C, 55-88

Abstract: We consider a stochastic process with long-range dependence perturbed by multiplicative noise. The marginal distributions of both the original process and the noise have regularly-varying tails, with tail indices α,α′>0, respectively. The original process is taken as the regularly-varying Karlin model, a recently investigated model that has long-range dependence characterized by a memory parameter β∈(0,1). We establish limit theorems for the extremes of the model, and reveal a phase transition. In terms of the limit there are three different regimes: signal-dominance regime α<α′β, noise-dominance regime α>α′β, and critical regime α=α′β. As for the proof, we actually establish the same phase-transition phenomena for the so-called Poisson–Karlin model with multiplicative noise defined on generic metric spaces, and apply a Poissonization method to establish the limit theorems for the one-dimensional case as a consequence.

Keywords: Random sup-measure; Random closed set; Stationary process; Point-process convergence; Regular variation; Long-range dependence (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1016/j.spa.2021.10.007

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