Applied Stochastic Models in Business and Industry最新文献

{"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}

{"title":"Using Multiple Regression Models and Methods to Estimate Fatigue-Life Distributions and Construct Constant-Life Diagrams","authors":"Diaz F. Aksioma,&nbsp;William Q. Meeker,&nbsp;Huaiqing Wu","doi":"10.1002/asmb.70074","DOIUrl":"https://doi.org/10.1002/asmb.70074","url":null,"abstract":"<p>Fatigue is the most common reliability failure mechanism and has been studied widely since the 19th century. Material specimens are used in laboratory experiments to obtain fatigue test data. An <i>S-N</i> curve is used to depict the relationship between the stress (or strain) <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>S</mi>\u0000 </mrow>\u0000 <annotation>$$ S $$</annotation>\u0000 </semantics></math> and the number of cycles to failure <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>N</mi>\u0000 </mrow>\u0000 <annotation>$$ N $$</annotation>\u0000 </semantics></math>. Statistical methods are used to fit an <i>S-N</i> relationship that can be used further to estimate properties of fatigue-life and fatigue-strength distributions. In particular, one can obtain estimates and confidence intervals for distribution quantiles and failure probabilities. Likelihood-based and Bayesian inference methods are the foundational methods for statistical estimation and quantification of statistical uncertainty. Improvements in computing technology (hardware, software, and computational methods) have made it practicable to use these methods for important applications. This paper uses these foundational methods to model fatigue life and fatigue strength as a function of the experimental variables stress amplitude, mean stress, and stress ratio, extending and importantly improving methods currently used for such applications. We illustrate the methods with two different data sets. The first example is based on <i>S-N</i> test data of a composite material widely used to manufacture wind turbine blades, where the fatigue-life model is specified, and the fatigue-strength is induced. The second example is based on <i>S-N</i> test data of an aluminum alloy commonly used in aerospace applications. Because of the complicated features of the fatigue life data for this example, we use a specified fatigue-strength model and show how it can be used to make inferences about the corresponding fatigue-life model. Finally, we show how to use these multiple regression models to obtain constant-life diagrams (CLDs), an engineering tool that provides a visual representation of the quantiles of a fatigue-life distribution as a function of stress amplitude and mean stress. We compare CLDs based on multiple regression models with CLDs obtained by using separate simple regression models for each level of stress ratio.</p>","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"42 2","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/asmb.70074","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147288300","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":"Constructing a Sample-Size Adaptable Sampling System Based on a Third-Generation Process Capability Index","authors":"To-Cheng Wang","doi":"10.1002/asmb.70076","DOIUrl":"https://doi.org/10.1002/asmb.70076","url":null,"abstract":"<div>\u0000 \u0000 <p>Acceptance sampling plans (ASP) help practitioners economically and efficiently verify product quality, serving as a widely applied statistical quality control method. Among ASPs, the simplest form is the single sampling plan (SSP). Recently, acceptance sampling systems incorporating two SSPs as decision rules for lot disposition have gained significant attention due to their superior performance. Depending on the switching mechanism for decision rules, acceptance sampling systems can be categorized into quick switching systems and two-plan sampling systems (TSS), where TSS exhibits greater flexibility and adaptability in its rule-switching mechanism. In this study, we construct a TSS with a sample size adjustment mechanism and integrate it with a third-generation process capability index. Detailed investigation and analysis reveal that the proposed method provides more substantial incentives for suppliers, as its properties penalize suppliers who submit poor-quality lots through increased sample size while rewarding suppliers who submit high-quality lots by requiring smaller sample sizes. Finally, we demonstrate the application of the proposed method through a practical case study.</p>\u0000 </div>","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"42 2","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147288322","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 GARCH Model Driven by Fractional Brownian Motion","authors":"Yuecai Han,&nbsp;Weiying Wang,&nbsp;Dingwen Zhang","doi":"10.1002/asmb.70071","DOIUrl":"https://doi.org/10.1002/asmb.70071","url":null,"abstract":"<div>\u0000 \u0000 <p>This article presents a novel extension of the GARCH model incorporating weighted liquidity, modeled by fractional Brownian motion. The existence of a stationary solution is proven, and the higher-order moments are calculated to illustrate the statistical properties of the model. Analysis of the auto-correlation function of the squared process confirms the long-term memory characteristic of the model. Numerical simulations are employed to validate the theoretical findings, demonstrating the significance of the model in the financial market.</p>\u0000 </div>","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"42 2","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147315442","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}