{"title":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"42 3","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148099131","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}
{"title":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"42 3","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148099540","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}
{"title":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"42 3","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148093745","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}
{"title":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"42 3","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148093918","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}
{"title":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"42 3","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148093885","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}
{"title":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"42 3","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148079633","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}
{"title":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"42 3","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148079634","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}
{"title":"Control Charts for Detecting Linear Drifts in Multivariate Process Mean and Covariance Matrix","authors":"Runzi Liao, Fan Yi, Xiulin Xie","doi":"10.1002/asmb.70088","DOIUrl":"https://doi.org/10.1002/asmb.70088","url":null,"abstract":"<div>\u0000 \u0000 <p>Statistical process control (SPC) charts are widely used in various fields to detect distributional changes in sequential processes. Traditional SPC charts are primarily designed to identify abrupt changes in process parameters, such as sudden shifts in the mean or variance. However, many real-world applications involve gradual changes over time, commonly referred to as drifts. This paper develops three change-point detection charts for identifying linear drifts in the mean vector, the covariance matrix, and both simultaneously in a multivariate process. The proposed charts are constructed based on the generalized likelihood ratio statistic and change-point detection techniques. These methods do not require pre-specification of procedure parameters and provide an estimate of the change-point location once a signal is given. Numerical studies demonstrate that the proposed charts are more effective in detecting linear drifts in the process mean and/or covariance matrix compared to conventional control charts designed for detecting abrupt changes.</p>\u0000 </div>","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"42 2","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147715304","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}
{"title":"Multi-Regional Diffusion Modes of Emerging Technologies: Modeling and Simulation Under Technological Learning Uncertainty","authors":"Yaru Zhang","doi":"10.1002/asmb.70086","DOIUrl":"https://doi.org/10.1002/asmb.70086","url":null,"abstract":"<div>\u0000 \u0000 <p>Technological learning and diffusion effects intrinsically drive the development of emerging technology capabilities. However, technological learning is fraught with significant uncertainty, and the spatiotemporal diffusion process across heterogeneous regions adds complexity to crafting effective adoption strategies. Existing research has seldom explored the multi-regional diffusion modes of emerging technologies under such technological learning uncertainty. Accordingly, this study develops a multi-regional system optimization model that endogenizes both uncertain technological learning and spatial diffusion effects. Through scenario simulations, the impacts of three distinct diffusion modes (one-way, circular, and unintentional) on the adoption pathways of emerging technologies are analyzed and compared. The simulation results reveal that: (1) Reducing learning uncertainty, shortening inter-regional distance, enhancing the technology diffusion effect, and policymakers adopting a more aggressive risk attitude can significantly promote the adoption of emerging technologies. (2) Following the technological spillover of an emerging technology, the diffusion of an existing technology can influence its diffusion path in all three modes. (3) The circular diffusion mode was the most economical strategy from a total system cost perspective and demonstrated the strongest resilience to the uncertainties associated with technological learning. These findings provide valuable theoretical insights for policymakers to design robust strategies for deploying emerging technologies across regions. This study contributes to a better understanding of the technology diffusion process under uncertainty and presents a framework for enhancing diffusion efficiency and mitigating systemic risks.</p>\u0000 </div>","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"42 2","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147715267","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}
{"title":"Information-Driven Modeling of Energy Markets: An Unbalanced Wasserstein Barycenter Approach","authors":"Carlo Mari, Emiliano Mari, Cristiano Baldassari","doi":"10.1002/asmb.70080","DOIUrl":"https://doi.org/10.1002/asmb.70080","url":null,"abstract":"<p>A novel methodology is proposed for jointly modeling the price dynamics of natural gas and electricity by integrating graph-based Machine Learning and optimal transport theory. The framework combines visibility graph embeddings with the Wasserstein barycenter to uncover latent structures and asymmetric dependencies between the two interconnected energy markets. Log-return time series are first transformed into visibility graphs and then embedded into high-dimensional vector spaces, where complex temporal and structural patterns become more discernible. In the embedding space, an information-driven Wasserstein barycenter is computed by optimizing the barycenter weights via Shannon entropy maximization. This procedure reveals an asymmetric balance between the two markets, with natural gas exerting a structurally dominant influence. To characterize the joint stochastic dynamics, a Gaussian Mixture Model is fitted to the thus determined unbalanced Wasserstein barycenter using maximum likelihood estimation via the Expectation–Maximization algorithm. An additional Gaussian component is introduced for each commodity to capture market-specific behavior. The resulting model can be calibrated to match the first four moments of the empirical log-return distributions and their observed correlation. Applied to Italian market data from 2019 to 2023, a period marked by extreme volatility and systemic shocks, the methodology accurately reproduces both common dynamics and idiosyncratic deviations. The analysis reveals that the entropy-optimal barycentric weights are <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msub>\u0000 <mi>λ</mi>\u0000 <mi>ng</mi>\u0000 </msub>\u0000 <mo>=</mo>\u0000 <mn>0.65</mn>\u0000 </mrow>\u0000 <annotation>$$ {lambda}_{mathrm{ng}}=0.65 $$</annotation>\u0000 </semantics></math> for natural gas and <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msub>\u0000 <mi>λ</mi>\u0000 <mi>el</mi>\u0000 </msub>\u0000 <mo>=</mo>\u0000 <mn>0.35</mn>\u0000 </mrow>\u0000 <annotation>$$ {lambda}_{mathrm{el}}=0.35 $$</annotation>\u0000 </semantics></math> for electricity, highlighting a dominant role of the natural gas market in the joint representation. Compared with a comprehensive benchmark of GARCH-type models, the proposed framework exhibits markedly superior empirical performance. The approach provides a robust, interpretable, and adaptable tool for risk analysis, derivative pricing, and the study of structural interactions across energy markets.</p>","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"42 2","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/asmb.70080","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147566198","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}
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