Krzysztof Dȩbicki, Enkelejd Hashorva, Zbigniew Michna
{"title":"关于伯曼函数","authors":"Krzysztof Dȩbicki, Enkelejd Hashorva, Zbigniew Michna","doi":"10.1007/s11009-023-10059-6","DOIUrl":null,"url":null,"abstract":"<p>Let <span>\\(Z(t)= \\exp \\left( \\sqrt{ 2} B_H(t)- \\left|t \\right|^{2H}\\right) , t\\in \\mathbb {R}\\)</span> with <span>\\(B_H(t),t\\in \\mathbb {R}\\)</span> a standard fractional Brownian motion (fBm) with Hurst parameter <span>\\(H \\in (0,1]\\)</span> and define for <i>x</i> non-negative the Berman function </p><span>$$\\begin{aligned} \\mathcal {B}_{Z}(x)= \\mathbb {E} \\left\\{ \\frac{ \\mathbb {I} \\{ \\epsilon _0(RZ) > x\\}}{ \\epsilon _0(RZ)}\\right\\} \\in (0,\\infty ), \\end{aligned}$$</span><p>where the random variable <i>R</i> independent of <i>Z</i> has survival function <span>\\(1/x,x\\geqslant 1\\)</span> and </p><span>$$\\begin{aligned} \\epsilon _0(RZ) = \\int _{\\mathbb {R}} \\mathbb {I}{\\left\\{ RZ(t)> 1\\right\\} }{dt} . \\end{aligned}$$</span><p>In this paper we consider a general random field (rf) <i>Z</i> that is a spectral rf of some stationary max-stable rf <i>X</i> and derive the properties of the corresponding Berman functions. In particular, we show that Berman functions can be approximated by the corresponding discrete ones and derive interesting representations of those functions which are of interest for Monte Carlo simulations presented in this article.</p>","PeriodicalId":18442,"journal":{"name":"Methodology and Computing in Applied Probability","volume":"10 1","pages":""},"PeriodicalIF":1.0000,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"On Berman Functions\",\"authors\":\"Krzysztof Dȩbicki, Enkelejd Hashorva, Zbigniew Michna\",\"doi\":\"10.1007/s11009-023-10059-6\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Let <span>\\\\(Z(t)= \\\\exp \\\\left( \\\\sqrt{ 2} B_H(t)- \\\\left|t \\\\right|^{2H}\\\\right) , t\\\\in \\\\mathbb {R}\\\\)</span> with <span>\\\\(B_H(t),t\\\\in \\\\mathbb {R}\\\\)</span> a standard fractional Brownian motion (fBm) with Hurst parameter <span>\\\\(H \\\\in (0,1]\\\\)</span> and define for <i>x</i> non-negative the Berman function </p><span>$$\\\\begin{aligned} \\\\mathcal {B}_{Z}(x)= \\\\mathbb {E} \\\\left\\\\{ \\\\frac{ \\\\mathbb {I} \\\\{ \\\\epsilon _0(RZ) > x\\\\}}{ \\\\epsilon _0(RZ)}\\\\right\\\\} \\\\in (0,\\\\infty ), \\\\end{aligned}$$</span><p>where the random variable <i>R</i> independent of <i>Z</i> has survival function <span>\\\\(1/x,x\\\\geqslant 1\\\\)</span> and </p><span>$$\\\\begin{aligned} \\\\epsilon _0(RZ) = \\\\int _{\\\\mathbb {R}} \\\\mathbb {I}{\\\\left\\\\{ RZ(t)> 1\\\\right\\\\} }{dt} . \\\\end{aligned}$$</span><p>In this paper we consider a general random field (rf) <i>Z</i> that is a spectral rf of some stationary max-stable rf <i>X</i> and derive the properties of the corresponding Berman functions. In particular, we show that Berman functions can be approximated by the corresponding discrete ones and derive interesting representations of those functions which are of interest for Monte Carlo simulations presented in this article.</p>\",\"PeriodicalId\":18442,\"journal\":{\"name\":\"Methodology and Computing in Applied Probability\",\"volume\":\"10 1\",\"pages\":\"\"},\"PeriodicalIF\":1.0000,\"publicationDate\":\"2024-01-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Methodology and Computing in Applied Probability\",\"FirstCategoryId\":\"100\",\"ListUrlMain\":\"https://doi.org/10.1007/s11009-023-10059-6\",\"RegionNum\":4,\"RegionCategory\":\"数学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"STATISTICS & PROBABILITY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Methodology and Computing in Applied Probability","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.1007/s11009-023-10059-6","RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"STATISTICS & PROBABILITY","Score":null,"Total":0}
Let \(Z(t)= \exp \left( \sqrt{ 2} B_H(t)- \left|t \right|^{2H}\right) , t\in \mathbb {R}\) with \(B_H(t),t\in \mathbb {R}\) a standard fractional Brownian motion (fBm) with Hurst parameter \(H \in (0,1]\) and define for x non-negative the Berman function
In this paper we consider a general random field (rf) Z that is a spectral rf of some stationary max-stable rf X and derive the properties of the corresponding Berman functions. In particular, we show that Berman functions can be approximated by the corresponding discrete ones and derive interesting representations of those functions which are of interest for Monte Carlo simulations presented in this article.
期刊介绍:
Methodology and Computing in Applied Probability will publish high quality research and review articles in the areas of applied probability that emphasize methodology and computing. Of special interest are articles in important areas of applications that include detailed case studies. Applied probability is a broad research area that is of interest to many scientists in diverse disciplines including: anthropology, biology, communication theory, economics, epidemiology, finance, linguistics, meteorology, operations research, psychology, quality control, reliability theory, sociology and statistics.
The following alphabetical listing of topics of interest to the journal is not intended to be exclusive but to demonstrate the editorial policy of attracting papers which represent a broad range of interests:
-Algorithms-
Approximations-
Asymptotic Approximations & Expansions-
Combinatorial & Geometric Probability-
Communication Networks-
Extreme Value Theory-
Finance-
Image Analysis-
Inequalities-
Information Theory-
Mathematical Physics-
Molecular Biology-
Monte Carlo Methods-
Order Statistics-
Queuing Theory-
Reliability Theory-
Stochastic Processes