Applied Stochastic Models in Business and Industry最新文献

Incorporating Asymmetric Loss for Real Estate Prediction With Area-Level Spatial Data

IF 1.3 4区数学

Applied Stochastic Models in Business and Industry Pub Date : 2025-04-02 DOI: 10.1002/asmb.70009

Vaidehi Dixit, Scott H. Holan, Christopher K. Wikle

引用次数: 0

Hazard Rate Order Between Parallel Systems With Multiple Types of Scaled Components

IF 1.3 4区数学

Applied Stochastic Models in Business and Industry Pub Date : 2025-03-27 DOI: 10.1002/asmb.70008

Khaled Masoumifard, Abedin Haidari, Nuria Torrado

{"title":"Hazard Rate Order Between Parallel Systems With Multiple Types of Scaled Components","authors":"Khaled Masoumifard, Abedin Haidari, Nuria Torrado","doi":"10.1002/asmb.70008","DOIUrl":"https://doi.org/10.1002/asmb.70008","url":null,"abstract":"<div>\u0000 \u0000 <p>This study delves into the comparison of two parallel systems, each composed of multiple component types following the scale models with a shared baseline distribution. Under some assumptions imposed on the baseline distribution, it is shown that a restricted version of the <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>p</mi>\u0000 </mrow>\u0000 <annotation>$$ p $$</annotation>\u0000 </semantics></math>-larger order between the scale parameter vectors implies the hazard rate order between the system lifetimes, provided that the allocation vectors of the two systems are the same. Additionally, we explore scenarios where one system embodies heterogeneous scale parameters, whereas the other adopts homogeneous ones, examining the hazard rate order between their lifetimes. For the case when the scale vectors of the two systems are the same, it is shown under some assumptions on the baseline distribution that the weak supermajorization order between the allocation vectors results in the reversed hazard rate order between the system lifetimes. Under more restrictions on the allocation vectors, an extension of this result to the likelihood ratio order is also established. Our discussion also highlights the fulfillment of these assumptions by well-known lifetime distributions such as Feller-Pareto, generalized gamma, power-generalized Weibull, exponentiated Weibull, and half-normal distributions. The findings of this work contribute to addressing gaps in the understanding of stochastic orderings between parallel systems and further refine prior research in this domain. Furthermore, the results provide a foundation for practical applications, such as optimizing resource allocation and reliability assessment in engineering and operational contexts.</p>\u0000 </div>","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"41 2","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143717359","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

No-Arbitrage Valuation of Contingent Claims Depending on an Untradeable Asset

IF 1.3 4区数学

Applied Stochastic Models in Business and Industry Pub Date : 2025-03-23 DOI: 10.1002/asmb.70007

Erindi Allaj

引用次数: 0

Digital Twins: A Revolution in Modeling and Simulation

IF 1.3 4区数学

Applied Stochastic Models in Business and Industry Pub Date : 2025-03-13 DOI: 10.1002/asmb.70006

Jean-Michel Poggi, Murat Caner Testik

引用次数: 0

Pareto Optimal Proxy Metrics

IF 1.3 4区数学

Applied Stochastic Models in Business and Industry Pub Date : 2025-03-10 DOI: 10.1002/asmb.70003

Alessandro Zito, Dylan Greaves, Jacopo Soriano, Lee Richardson

{"title":"Pareto Optimal Proxy Metrics","authors":"Alessandro Zito, Dylan Greaves, Jacopo Soriano, Lee Richardson","doi":"10.1002/asmb.70003","DOIUrl":"https://doi.org/10.1002/asmb.70003","url":null,"abstract":"<div>\u0000 \u0000 <p>North star metrics and online experimentation play a central role in how technology companies improve their products. In many practical settings, however, evaluating experiments based on the north star metric directly can be difficult. The two most significant issues are (1) low sensitivity of the north star metric and (2) differences between the short-term and long-term impact on it. A common solution is to rely on proxy metrics rather than the north star in experiment evaluation and launch decisions. Existing literature on proxy metrics concentrates mainly on the estimation of the long-term impact from short-term experimental data. In this article, instead, we focus on the trade-off between the estimation of the long-term impact and the sensitivity in the short term. In particular, we propose the Pareto optimal proxy metrics method, which simultaneously optimizes prediction accuracy and sensitivity. We also give a multi-objective optimization algorithm to solve our specific problem. We apply our methodology to experiments from a large industrial recommendation system, and found proxy metrics that are eight times more sensitive than the north star and consistently moved in the same direction, increasing the velocity and the quality of the decisions to launch new features.</p>\u0000 </div>","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"41 2","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143595236","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

Is There a Future for Stochastic Modeling in Business and Industry in the Era of Machine Learning and Artificial Intelligence?

IF 1.3 4区数学

Applied Stochastic Models in Business and Industry Pub Date : 2025-03-10 DOI: 10.1002/asmb.70004

Fabrizio Ruggeri, David Banks, William S. Cleveland, Nicholas I. Fisher, Marcos Escobar-Anel, Paolo Giudici, Emanuela Raffinetti, Roger W. Hoerl, Dennis K. J. Lin, Ron S. Kenett, Wai Keung Li, Philip L. H. Yu, Jean-Michel Poggi, Marco S. Reis, Gilbert Saporta, Piercesare Secchi, Rituparna Sen, Ansgar Steland, Zhanpan Zhang

{"title":"Is There a Future for Stochastic Modeling in Business and Industry in the Era of Machine Learning and Artificial Intelligence?","authors":"Fabrizio Ruggeri, David Banks, William S. Cleveland, Nicholas I. Fisher, Marcos Escobar-Anel, Paolo Giudici, Emanuela Raffinetti, Roger W. Hoerl, Dennis K. J. Lin, Ron S. Kenett, Wai Keung Li, Philip L. H. Yu, Jean-Michel Poggi, Marco S. Reis, Gilbert Saporta, Piercesare Secchi, Rituparna Sen, Ansgar Steland, Zhanpan Zhang","doi":"10.1002/asmb.70004","DOIUrl":"https://doi.org/10.1002/asmb.70004","url":null,"abstract":"<div>\u0000 \u0000 <p>The paper arises from the experience of <i>Applied Stochastic Models in Business and Industry</i> which has seen, over the years, more and more contributions related to Machine Learning rather than to what was intended as a stochastic model. The very notion of a stochastic model (e.g., a Gaussian process or a Dynamic Linear Model) can be subject to change: What is a Deep Neural Network if not a stochastic model? The paper presents the views, supported by examples, of distinguished researchers in the field of business and industrial statistics. They are discussing not only whether there is a future for traditional stochastic models in the era of Machine Learning and Artificial Intelligence, but also how these fields can interact and gain new life for their development.</p>\u0000 </div>","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"41 2","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143595155","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

Redundancy Allocation Problem in k-Out-Of-n Systems With Dependent and Heterogeneous Components

IF 1.3 4区数学

Applied Stochastic Models in Business and Industry Pub Date : 2025-03-03 DOI: 10.1002/asmb.2932

Zohreh Zare, Somayeh Zarezadeh, Mahmood Kharrati-Kopaei

引用次数: 0

Mean Distances and Dependence Structures for Lifetimes of Systems With Shared Components

IF 1.3 4区数学

Applied Stochastic Models in Business and Industry Pub Date : 2025-02-24 DOI: 10.1002/asmb.70002

Marco Capaldo, Antonio Di Crescenzo, Franco Pellerey

引用次数: 0

A Framework for Product Life Cycle Management Based Digital Twin Implementation in the Aerospace Industry

IF 1.3 4区数学

Applied Stochastic Models in Business and Industry Pub Date : 2025-02-19 DOI: 10.1002/asmb.70001

Busra Oksuz Gurdal, Ozlem Muge Testik

{"title":"A Framework for Product Life Cycle Management Based Digital Twin Implementation in the Aerospace Industry","authors":"Busra Oksuz Gurdal, Ozlem Muge Testik","doi":"10.1002/asmb.70001","DOIUrl":"https://doi.org/10.1002/asmb.70001","url":null,"abstract":"<p>As an emerging technology, digital twin (DT) studies are gaining momentum in both academia and industry. Specifically, the aerospace industry can benefit significantly from the implementation of DT technology since its products and processes are complex, technically challenging, and costly. DTs enable a comprehensive technology integration capacity and holistic approach in the product life cycle. However, for simplification, implementations of DT to processes in the aerospace industry are often handled independently without integration with other related processes. In this study, we propose a methodological framework to integrate different processes throughout the essential parts of aircraft's life cycle. In pursuit of creating a DT of the system for managing the life cycle of aircraft, all aspects and processes have been thoroughly examined. Ten main components for the management of DTs are identified. Statistical and stochastic approaches for enhancing the analytical capabilities of DTs are discussed. Within the scope of Product Life Cycle Management and from the perspective of Systems Engineering, we advocate creating the DT of an aircraft by combining the DTs for each component through a digital thread.</p>","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"41 2","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/asmb.70001","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143446868","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

Modelling Task Durations Towards Automated, Big Data, Process Mining

IF 1.3 4区数学

Applied Stochastic Models in Business and Industry Pub Date : 2025-02-11 DOI: 10.1002/asmb.2933

Malcolm Faddy, Lingkai Yang, Sally McClean, Mark Donnelly, Kashaf Khan, Kevin Burke

{"title":"Modelling Task Durations Towards Automated, Big Data, Process Mining","authors":"Malcolm Faddy, Lingkai Yang, Sally McClean, Mark Donnelly, Kashaf Khan, Kevin Burke","doi":"10.1002/asmb.2933","DOIUrl":"https://doi.org/10.1002/asmb.2933","url":null,"abstract":"<p>Business processes are generally time-sensitive, impacting factors such as customer expectations, cost efficiencies, compliance requirements, supply chain constraints, and timely decision-making. Time analysis is therefore crucial for customer understanding and process congestion minimisation. Existing process mining methods mainly employ basic statistics, process discovery and data mining techniques. These approaches often lack a structured model or profile to characterise the data related to the duration of individual process tasks. Consequently, it can be difficult to comprehensively understand critical observations such as trends, peaks, and valleys of task durations. This paper proposes a parsimonious generic representation of task duration data that addresses these limitations. A mixture model comprising gamma, uniform and exponential distributions is proposed that allows for peaked components corresponding to durations terminating near a particular value (the peak) with, in addition, flatter components for durations terminating more randomly between the peaks. The modelling is validated using examples from patient billing and the telecom industry. In each scenario, the corresponding fitted models offer a good representation of the underlying process tasks. The model can therefore be used to improve knowledge of these tasks in terms of the mixture components and what they might represent, such as the root causes of task termination. The paper also considers information criteria more appropriate for large data sets where very small effects can appear “significant” using techniques developed for smaller data sets.</p>","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"41 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/asmb.2933","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143380549","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