Applied Stochastic Models in Business and Industry最新文献_第3页

Foreword to the Special Issue on Energy Finance and Climate Change 能源金融与气候变化特刊前言

IF 1.3 4区数学

Applied Stochastic Models in Business and Industry Pub Date : 2024-12-03 DOI: 10.1002/asmb.2909

Roberto Baviera, Carlo Sgarra, Tiziano Vargiolu, Rituparna Sen

引用次数: 0

Is (Independent) Subordination Relevant in Equity Derivatives? 独立）从属关系与股票衍生品相关吗？

IF 1.4 4区数学

Applied Stochastic Models in Business and Industry Pub Date : 2024-11-18 DOI: 10.1002/asmb.2904

Michele Azzone, Roberto Baviera

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Renewable Energy Investments, Support Schemes and the Dirty Option 可再生能源投资，支持计划和肮脏的选择

IF 1.3 4区数学

Applied Stochastic Models in Business and Industry Pub Date : 2024-10-26 DOI: 10.1002/asmb.2901

Domenico De Giovanni, Elena Iakimova

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Bayesian Sequential Learning and Decision Making in Bike-Sharing Systems 共享单车系统中的贝叶斯顺序学习与决策

IF 1.3 4区数学

Applied Stochastic Models in Business and Industry Pub Date : 2024-10-23 DOI: 10.1002/asmb.2888

Tevfik Aktekin, Bumsoo Kim, Luis J. Novoa, Babak Zafari

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Framework for Cyber Risk Loss Distribution of Client-Server Networks: A Bond Percolation Model and Industry Specific Case Studies 客户端-服务器网络的网络风险损失分布框架：债券渗透模型和特定行业案例研究

IF 1.3 4区数学

Applied Stochastic Models in Business and Industry Pub Date : 2024-10-08 DOI: 10.1002/asmb.2896

Stefano Chiaradonna, Petar Jevtić, Nicolas Lanchier, Sasa Pesic

{"title":"Framework for Cyber Risk Loss Distribution of Client-Server Networks: A Bond Percolation Model and Industry Specific Case Studies","authors":"Stefano Chiaradonna, Petar Jevtić, Nicolas Lanchier, Sasa Pesic","doi":"10.1002/asmb.2896","DOIUrl":"https://doi.org/10.1002/asmb.2896","url":null,"abstract":"<div>\u0000 \u0000 <p>Cyber risk has emerged as a significant threat to businesses that have increasingly relied on new and existing information technologies (IT). Across various businesses in different industries and sectors, a distinct pattern of IT network architectures, such as the client-server network architecture, may, in principle, expose those businesses, which share it, to similar cyber risks. That is why in this article, we propose a probabilistic structural framework for loss assessments of cyber risks on the class of client-server network architectures with <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>K</mi>\u0000 </mrow>\u0000 <annotation>$$ K $$</annotation>\u0000 </semantics></math> different client types. To our knowledge, there exist no theoretical models of an aggregate loss distribution for cyber risk in this setting. With this structural framework via the exact mean and variance of losses, we demonstrate how the changing cybersecurity environment of a business's IT network impacts the loss distribution. Furthermore, our framework provides insights into better investment strategies for cybersecurity protection on the client-server network. Motivated by cyberattacks across industries, we apply our framework to four case studies that utilize the client-server network architecture. Our first application is implantable medical devices in healthcare. Our second application is the smart buildings domain. Third, we present an application for ride-sharing services such as Uber and Lyft. The fourth is the application of vehicle-to-vehicle cooperation in traffic management. The results are corresponding exact means and variances of cyber risk loss distributions parameterized by various cybersecurity parameters allowing for liability assessments and decisions in cybersecurity protection investments.</p>\u0000 </div>","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"40 6","pages":"1712-1733"},"PeriodicalIF":1.3,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142868212","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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Limiting Behavior of Mixed Coherent Systems With Lévy-Frailty Marshall–Olkin Failure Times 具有 Lévy-Frailty Marshall-Olkin 故障时间的混合相干系统的极限行为

IF 1.3 4区数学

Applied Stochastic Models in Business and Industry Pub Date : 2024-10-08 DOI: 10.1002/asmb.2893

Guido Lagos, Javiera Barrera, Pablo Romero, Juan Valencia

{"title":"Limiting Behavior of Mixed Coherent Systems With Lévy-Frailty Marshall–Olkin Failure Times","authors":"Guido Lagos, Javiera Barrera, Pablo Romero, Juan Valencia","doi":"10.1002/asmb.2893","DOIUrl":"https://doi.org/10.1002/asmb.2893","url":null,"abstract":"<div>\u0000 \u0000 <p>In this article, we show a limit result for the reliability function of a system—that is, the probability that the whole system is still operational after a certain given time—when the number of components of the system grows to infinity. More specifically, we consider a sequence of mixed coherent systems whose components are homogeneous and non-repairable, with failure-times governed by a Lévy-Frailty Marshall–Olkin (LFMO) distribution—a distribution that allows simultaneous component failures. We show that under integrability conditions the reliability function converges to the probability of a first-passage time of a Lévy subordinator process. To the best of our knowledge, this is the first result to tackle the asymptotic behavior of the reliability function as the number of components of the system grows. To illustrate our approach, we give an example of a parametric family of reliability functions where the system failure time converges in distribution to an exponential random variable, and give computational experiments testing convergence.</p>\u0000 </div>","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"40 5","pages":"1229-1244"},"PeriodicalIF":1.3,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142447659","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Foreword to the Special Issue on Mathematical Methods in Reliability (MMR23) 可靠性数学方法特刊（MMR23）前言

IF 1.3 4区数学

Applied Stochastic Models in Business and Industry Pub Date : 2024-10-08 DOI: 10.1002/asmb.2894

Félix Belzunce, Jorge Navarro

{"title":"Foreword to the Special Issue on Mathematical Methods in Reliability (MMR23)","authors":"Félix Belzunce, Jorge Navarro","doi":"10.1002/asmb.2894","DOIUrl":"https://doi.org/10.1002/asmb.2894","url":null,"abstract":"<p>The 12th International Conference on Mathematical Methods in Reliability (MMR 2023, May 30–June 2, Murcia Spain) continues a distinguished tradition of bringing together leading experts, researchers, and practitioners from various fields to explore cutting-edge advancements in reliability theory and applications. Since its inception, the MMR conference series has provided a premier platform for exchanging ideas and promoting collaboration across a wide array of disciplines, including mathematics, engineering, statistics, operations research, and computer science.</p><p>Reliability theory plays a crucial role in designing and analyzing complex systems, where safety, dependability, and risk assessment are paramount. As technological advancements accelerate and systems become increasingly intricate, the demand for robust mathematical models and methods to ensure system reliability is more critical than ever.</p><p>As the world faces new challenges in ensuring the resilience and reliability of critical infrastructures and technologies, the MMR conference continues to inspire innovation and foster collaboration. We hope this collection of papers will provide readers with valuable insights and stimulate further research in this vibrant and essential field.</p>","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"40 5","pages":"1206-1208"},"PeriodicalIF":1.3,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/asmb.2894","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142447658","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Application of Hawkes Volatility in the Observation of Filtered High-Frequency Price Process in Tick Structures 霍克斯波动率在指数结构中过滤高频价格过程观察中的应用

IF 1.3 4区数学

Applied Stochastic Models in Business and Industry Pub Date : 2024-10-03 DOI: 10.1002/asmb.2892

Kyungsub Lee

引用次数: 0

Pricing Cyber Insurance: A Geospatial Statistical Approach 网络保险定价：地理空间统计方法

IF 1.3 4区数学

Applied Stochastic Models in Business and Industry Pub Date : 2024-09-22 DOI: 10.1002/asmb.2891

L. V. Ballestra, V. D'Amato, P. Fersini, S. Forte, F. Greco

引用次数: 0

Regional Shopping Objectives in British Grocery Retail Transactions Using Segmented Topic Models 使用细分主题模型分析英国杂货零售交易中的区域购物目标

IF 1.4 4区数学

Applied Stochastic Models in Business and Industry Pub Date : 2024-09-17 DOI: 10.1002/asmb.2890

Mariflor Vega Carrasco, Mirco Musolesi, Jason O'Sullivan, Rosie Prior, Ioanna Manolopoulou

{"title":"Regional Shopping Objectives in British Grocery Retail Transactions Using Segmented Topic Models","authors":"Mariflor Vega Carrasco, Mirco Musolesi, Jason O'Sullivan, Rosie Prior, Ioanna Manolopoulou","doi":"10.1002/asmb.2890","DOIUrl":"https://doi.org/10.1002/asmb.2890","url":null,"abstract":"Understanding the customer behaviours behind transactional data has high commercial value in the grocery retail industry. Customers generate millions of transactions every day, choosing and buying products to satisfy specific shopping needs. Product availability may vary geographically due to local demand and local supply, thus driving the importance of analysing transactions within their corresponding store and regional context. Topic models provide a powerful tool in the analysis of transactional data, identifying topics that display frequently‐bought‐together products and summarising transactions as mixtures of topics. We use the segmented topic model (STM) to capture customer behaviours that are nested within stores. STM not only provides topics and transaction summaries but also topical summaries at the store level that can be used to identify regional topics. We summarise the posterior distribution of STM by post‐processing multiple posterior samples and selecting semantic modes represented as recurrent topics, and employ Gaussian process regression to model topic prevalence across British territory while accounting for spatial autocorrelation. We implement our methods on a dataset of transactional data from a major UK grocery retailer and demonstrate that shopping behaviours may vary regionally and nearby stores tend to exhibit similar regional demand.","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"19 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142253769","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0