Applied Stochastic Models in Business and Industry最新文献

{"title":"Control Charts for Detecting Linear Drifts in Multivariate Process Mean and Covariance Matrix","authors":"Runzi Liao,&nbsp;Fan Yi,&nbsp;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,&nbsp;Emiliano Mari,&nbsp;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}

{"title":"Forecasting High-Frequency Trade Durations: A Regime-Switching Approach With Flexible Hazard Functions","authors":"Yiing-Fei Tan,&nbsp;You-Beng Koh,&nbsp;Kok-Haur Ng","doi":"10.1002/asmb.70083","DOIUrl":"https://doi.org/10.1002/asmb.70083","url":null,"abstract":"<div>\u0000 \u0000 <p>This paper introduces a threshold stochastic conditional duration (TSCD) model to capture regime-switching behaviour in both the observed duration process and the latent process. Additionally, the model captures complex market dynamics using roller-coaster-shaped hazard functions derived from the extended generalised inverse Gaussian (EGIG) distribution. The model parameters are estimated using the simulation-based maximum likelihood method implemented via the sampling importance resampling algorithm, and validated through a simulation study. The empirical analysis employs trade duration data from the Apple incorporated and Tesla incorporated stocks. The results demonstrate that the TSCD model incorporated with EGIG distribution effectively captures distinct regime-switching behaviours in trade durations, characterised by different parameter sets across regimes in the in-sample analysis, yielding the highest log-likelihood value and the lowest Akaike information criterion and Bayesian information criterion scores amongst all benchmark models. For the out-of-sample forecasts, the TSCD_EGIG model consistently achieved the strongest performance, ranking first in most of the evaluated loss functions (mean squared forecast error and quasi-likelihood). The Diebold–Mariano test further provides robust evidence of significant differences in the relative predictive performance of the models. Time-at-risk forecasts across various risk levels are computed and evaluated using the Kupiec likelihood ratio test. Lastly, density forecasts are assessed through the probability integral transform technique.</p>\u0000 </div>","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":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147566197","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":"The Asymptotic Distribution of the Weighted Altham's Index in Log-Ratio Analysis","authors":"Antonello D'Ambra,&nbsp;Pietro Amenta","doi":"10.1002/asmb.70082","DOIUrl":"https://doi.org/10.1002/asmb.70082","url":null,"abstract":"<div>\u0000 \u0000 <p>Log-ratio analysis is a well-known framework for investigating and modeling compositional data. This method utilizes log-ratio transformations. The vectors connecting points on the maps illustrate the logarithmic relationships between data values in corresponding rows or columns. Correspondence analysis also creates a map where the proximity of points and other geometric features of the map reflect relationships between rows, between columns, and between rows and columns. Indeed, both methods share a similar theory, allowing for a graphical display of the association between the variables. While it is possible to verify in correspondence analysis the significance of the association between the variables, as well as between each row and column category, it seems not to be possible to perform the same inferential analyses within the log-ratio analysis. The investigative capabilities of the log-ratio analysis are then limited to graphical visualisation alone. To overcome this drawback, we introduce the asymptotic distribution of the weighted Altham's index (at the heart of the weighted log-Rratio analysis) under a Poissonian model and develop confidence circles for each row and column category of this approach.</p>\u0000 </div>","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"42 2","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147564985","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":"On the Prediction of Risky Asset Market Based on a Long Memory Model","authors":"Xiaoxia Sun,&nbsp;Shiyi Zheng","doi":"10.1002/asmb.70081","DOIUrl":"https://doi.org/10.1002/asmb.70081","url":null,"abstract":"<div>\u0000 \u0000 <p>In this paper, we focus on estimating some unknown parameters of a geometric bifractional Brownian motion. A geometric bifractional Brownian motion satisfies a stochastic differential equation driven by a bifractional Brownian motion. Firstly, using the method of quadratic variation for a Gaussian process and the maximum likelihood method, we give the estimators for the unknown parameters. Then, we prove the asymptotic properties of the estimators. Secondly, the Monte Carlo method is used for simulation. Compared with the single maximum likelihood estimation method, the results show that the method in this paper is effective, reliable, and superior. Finally, we conduct an empirical study of financial markets with real financial data from Danimer Scientific Inc-A (DNMR.N). By using path simulation, Euclidean distance and out-of-sample forecasting compared to other classical models, we effectively validate the superiority of the model in this paper in describing financial time series.</p>\u0000 </div>","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"42 2","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147564648","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":"Evaluating Biases in Large Language Models Over Time: A Framework With a GPT Case Study on Political Bias","authors":"Meltem Aksoy,&nbsp;Erik Weber,&nbsp;Jérôme Rutinowski,&nbsp;Niklas Jost,&nbsp;Markus Pauly","doi":"10.1002/asmb.70078","DOIUrl":"10.1002/asmb.70078","url":null,"abstract":"<p>Large Language Models (LLMs) have repeatedly been shown to reflect systematic biases. At the same time, commercial LLMs are updated at a rapid rate, often without notice to end-users, so that a bias profile captured today may already be outdated tomorrow. However, the literature still leans heavily on one-shot evaluations of single model versions, leaving a gap in our understanding of how biases evolve over time and how they should be monitored. We address this gap by introducing a framework for longitudinal evaluation of biases in LLMs, focusing on political bias as a case study. The framework is model-agnostic, reproducible, and user-friendly. It consists of (i) locking model versions via dated identifiers to guarantee temporal comparability, (ii) multi-prompt questionnaires on position statements to analyze potential biases; and (iii) a longitudinal statistical evaluation framework that quantifies and infers absolute bias and drifts between models. Moreover, we suggest conducting (iv) cross-questionnaire correlation analyses to reveal orthogonal biases, as well as (v) sensitivity analyses on the model's role-assignment mechanisms to analyze robustness to concrete instructions. All code, prompts, and outputs are openly available to facilitate replication and extension to other bias analyses. To illustrate the framework, we investigate the political biases and personality traits of ChatGPT, specifically comparing GPT-3.5, GPT-4, GPT-4o, and GPT-5.2. In addition, the ability of the models to emulate political viewpoints (e.g., liberal or conservative positions) is analyzed. Across 4000 generated answers, we observe clear political shifts between versions: While newer models appear less left-leaning, they still mimic progressive personality profiles and exhibit biases. These findings demonstrate the persistence and transformation of biases across updates, underlining the need for longitudinal monitoring.</p>","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"42 2","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/asmb.70078","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147562756","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}

{"title":"RETRACTION: Smart Contract for Electricity Transactions and Charge Settlements Using Blockchain","authors":"","doi":"10.1002/asmb.70079","DOIUrl":"10.1002/asmb.70079","url":null,"abstract":"<p>\u0000 <b>RETRACTION</b>: <span>J. Lu</span>, <span>S. Wu</span>, <span>H. Cheng</span>, <span>B. Song</span>, and <span>Z. Xiang</span>, “ <span>Smart Contract for Electricity Transactions and Charge Settlements Using Blockchain</span>,” <i>Applied Stochastic Models in Business and Industry</i> <span>37</span>, no. <span>3</span> (<span>2021</span>): <span>442</span>–<span>453</span>, https://doi.org/10.1002/asmb.2570</p><p>The above article, published online on 11 September 2020 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the journal Editor in Chief, Nalini Ravishanker; and John Wiley &amp; Sons Ltd. The retraction has been agreed due to major overlap with a previously published article from the same author group [Lu et al. (2020); https://doi.org/10.1111/coin.12388].</p>","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"42 2","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/asmb.70079","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147569485","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}

{"title":"Simple Thoughts About Applied Statistics in the Age of Data Science and Artificial Intelligence","authors":"Bovas Abraham,&nbsp;Asokan M. Variyath","doi":"10.1002/asmb.70073","DOIUrl":"https://doi.org/10.1002/asmb.70073","url":null,"abstract":"<div>\u0000 \u0000 <p>We consider some key concepts in the application of statistics to real world problem solving which are still relevant in the era of Big Data and Artificial Intelligence (AI). Also, we give an outline of some historical developments in industrial quality improvement where statistical methods are widely applied. In addition, we briefly discuss Big Data, Data Science, and some aspects of machine learning with emphasis on Statistical Thinking and ethical considerations.</p>\u0000 </div>","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"42 2","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147315623","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":"Applied Statistics in the Era of Artificial Intelligence: A Review and Vision","authors":"Jie Min,&nbsp;Xinyi Song,&nbsp;Simin Zheng,&nbsp;Caleb B. King,&nbsp;Xinwei Deng,&nbsp;Yili Hong","doi":"10.1002/asmb.70075","DOIUrl":"https://doi.org/10.1002/asmb.70075","url":null,"abstract":"<div>\u0000 \u0000 <p>The advent of artificial intelligence (AI) technologies has significantly changed many domains, including applied statistics. This review and vision paper explores the evolving role of applied statistics in the AI era, drawing from our experiences in engineering statistics. We begin by outlining the fundamental concepts and historical developments in applied statistics and tracing the rise of AI technologies. Subsequently, we review traditional areas of applied statistics, using examples from engineering statistics to illustrate key points. We then explore emerging areas in applied statistics, driven by recent technological advancements, highlighting examples from our recent projects. The paper discusses the symbiotic relationship between AI and applied statistics, focusing on how statistical principles can be employed to study the properties of AI models and enhance AI systems. We also examine how AI can advance applied statistics in terms of modeling and analysis. In conclusion, we reflect on the future role of statisticians. Our paper aims to shed light on the transformative impact of AI on applied statistics and inspire further exploration in this dynamic field.</p>\u0000 </div>","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"42 2","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147288323","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}