Applied Stochastic Models in Business and Industry最新文献

{"title":"Ransomware Detection Using Sample Entropy and Graphical Models: A Methodology for Explainable Artificial Intelligence (XAI) in Cybersecurity","authors":"Danilo Bruschi,&nbsp;Marzio De Corato,&nbsp;Alfio Ferrara,&nbsp;Silvia Salini","doi":"10.1002/asmb.70061","DOIUrl":"https://doi.org/10.1002/asmb.70061","url":null,"abstract":"<div>\u0000 \u0000 <p>Malware detection poses a critical challenge for both society and Business and Industry (B&amp;I), particularly given the necessity for secure digital transformation. Among various cybersecurity threats, ransomware has emerged as especially disruptive, capable of halting operations, interrupting business continuity, and causing significant financial damage. Recent research has increasingly leveraged machine learning (ML) techniques to detect ransomware using Hardware Performance Counters (HPCs)—special CPU registers that track low-level hardware activities. In this study, we first propose a Sample Entropy (SampEn)-based method for compressing HPC time series data. This method effectively reduces dimensionality while preserving essential behavioral patterns, thus making it particularly suitable for practical B&amp;I scenarios where accuracy and computational efficiency are crucial. Second, we investigate explainable algorithms for ransomware detection in B&amp;I contexts, emphasizing transparency and interpretability. To achieve this goal, we focus on graphical models, specifically Markov Random Fields (MRFs) and Bayesian Networks. We evaluate the performance of these explainable methods against a baseline comprising Elastic Net, Support Vector Machines (SVM) with a radial kernel, XGBoost, and Autoencoder models. Our results demonstrate that these graphical models provide consistent and interpretable outcomes, closely aligned with known ransomware behaviors.</p>\u0000 </div>","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"41 6","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145848193","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":"Discussing Cascading Failures: The Bursting Point Processes Approach","authors":"Maxim Finkelstein,&nbsp;Na Young Yoo,&nbsp;Ji Hwan Cha","doi":"10.1002/asmb.70060","DOIUrl":"https://doi.org/10.1002/asmb.70060","url":null,"abstract":"<div>\u0000 \u0000 <p>We discuss the new approach to modelling cascading failures from a probabilistic viewpoint based on exploding self-exciting point processes. It explains a <i>possible mechanism</i> of the cascade development. From the practical point of view, this approach might be oversimplified for modelling the flow of events (failures) in, for example, real-life power grids (as, e.g., not considering the specific network topology). However, even in this general form, it can be useful for overall modelling of the converging process of cascading failures and understanding the probabilistic nature of this interesting phenomenon. Three baseline processes are considered: the geometric process, the geometric-type process with a decreasing threshold after each event and the extended generalised Polya process.</p>\u0000 </div>","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"41 6","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145750597","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":"An Approach to Explore Consumer Behavior Patterns in Retail Markets Using Market Basket Analysis","authors":"Aarti Pardeshi,&nbsp;Yogesh Shahare,&nbsp;Kalpana Kumaran,&nbsp;Anand Muni Mishra,&nbsp;Piyush Kumar Shukla,&nbsp;Mohamed M. Hassan,&nbsp;Fayez Althobaiti","doi":"10.1002/asmb.70057","DOIUrl":"https://doi.org/10.1002/asmb.70057","url":null,"abstract":"<div>\u0000 \u0000 <p>Big data is becoming an integral part of everyday life. Data can be generated through various sources, and analyzing this data to generate revenue is the biggest challenge. The growth of grocery stores in the online and offline market requires retailers to analyze customer purchase behavior. Effective analysis can improve service quality, profitability, and customer satisfaction. This paper focuses on Market Basket Analysis (MBA), an efficient technique that identifies customer purchase behaviors. AAGI an Apriori-based algorithm is developed and used to analyze categorical data collected from grocery stores. The dataset consists of 10,233 transactions collected from the Raigad region Maharashtra, India and includes 125 unique items across dairy products, fruits, processed food, and vegetables. Association rules were generated using support values of 2%, 3%, 3.5%, and 4% and confidence values of 20%, 30%, and 40%. The best results were observed with the support of 3.5% and confidence of 30%, which produced 18 profound association rules. These findings inform targeted marketing strategies and cross-selling opportunities.</p>\u0000 </div>","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"41 6","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145618962","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":"Data Driven Investment Strategies Using Bayesian Inference in Regime-Switching Models","authors":"Eléonore Blanchard,&nbsp;Pierre-Olivier Goffard","doi":"10.1002/asmb.70058","DOIUrl":"https://doi.org/10.1002/asmb.70058","url":null,"abstract":"<p>This article presents the benefits of using Bayesian algorithms to fit regime-switching models to daily financial returns data in order to design trading strategies. Our study focuses on a Gaussian hidden Markov model (HMM). We show how the application of a simple smoothing technique preserves the hidden Markov structure and facilitates regime detection even in instances of highly volatile data. The effectiveness of a trading strategy, based on regime detection, may be hindered by a high rate of false signals, leading to numerous trades and, consequently, an escalation in transaction costs. By reducing variance through data smoothing, we enhance the persistence of regimes over time. We validate our statistical learning procedures using synthetic data prior to their application to real-world financial data.</p>","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"41 6","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/asmb.70058","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145619105","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":"Functional Data Regression on Distribution-Valued Data via Logarithm Derivative Quantile Transformation","authors":"Gianmarco Borrata,&nbsp;Antonio Balzanella,&nbsp;Rosanna Verde","doi":"10.1002/asmb.70059","DOIUrl":"https://doi.org/10.1002/asmb.70059","url":null,"abstract":"<p>In this paper, we introduce a new regression method tailored for data presented as distributions. Building on the latest advancements in Distributional Data Analysis (DDA), we propose a new regression model based on a transformation of quantile functions using Logarithmic Derivative Quantile (LDQ) functions. For each distributional variable <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msub>\u0000 <mrow>\u0000 <mi>X</mi>\u0000 </mrow>\u0000 <mrow>\u0000 <mi>j</mi>\u0000 </mrow>\u0000 </msub>\u0000 </mrow>\u0000 <annotation>$$ {X}_j $$</annotation>\u0000 </semantics></math> (where <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>j</mi>\u0000 <mo>=</mo>\u0000 <mn>1</mn>\u0000 <mo>,</mo>\u0000 <mi>…</mi>\u0000 <mo>,</mo>\u0000 <mi>p</mi>\u0000 </mrow>\u0000 <annotation>$$ j=1,dots, p $$</annotation>\u0000 </semantics></math>), we model the LDQ functions as functional data by applying smoothing B-splines at the points corresponding to the distributions' quantiles. The main contribution is the development of a regression model that considers functional regression coefficients. This allows for the consideration of distribution characteristics such as position, variability, and shape. Another contribution is the development of a robust procedure based on trimming distributions to reduce the instability of the tails and make more effective predictions. The proposed approach is corroborated by real environmental data. Cross-validation and bootstrap techniques have been employed to assess the effectiveness of both the new regression model and its robust variant.</p>","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"41 6","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/asmb.70059","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145619106","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":"On the Properties of the Weighted Mean Residual Life in Mixtures","authors":"F. G. Badía,&nbsp;M. D. Berrade Ursúa,&nbsp;J. H. Cha,&nbsp;H. Lee","doi":"10.1002/asmb.70055","DOIUrl":"https://doi.org/10.1002/asmb.70055","url":null,"abstract":"<p>This paper focuses on the weighted mean residual life (WMRL) in mixtures of time to failure distributions. WMRL is an aging index that accounts for transformations of nonnegative random variables. The time to failure of systems operating under changing environments is described by mixtures of distributions that capture the corresponding random effects. This study analyzes the preservation by mixtures of aging properties based on the WMRL and bending properties. The latter compare the WMRL of the mixture and the expected value of the WMRL of the distributions therein. We also analyze the combined effect of a frailty and lifetime functions in the case of mixtures following the proportional WMRL model. The results reveal the improved behavior in the WMRL of mixtures with respect to that in the sub-populations in the mixture. This pattern is relevant for the maintenance of systems.</p>","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"41 6","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/asmb.70055","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145522106","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":"A Leptokurtic-Form Birnbaum-Saunders Distribution With Applications to Finance","authors":"David Sánchez-Vega,&nbsp;Filidor Vilca,&nbsp;Camila Borelli Zeller,&nbsp;N. Balakrishnan","doi":"10.1002/asmb.70053","DOIUrl":"https://doi.org/10.1002/asmb.70053","url":null,"abstract":"<p>We propose here a new multivariate Birnbaum-Saunders (BS-type) distribution characterized by its leptokurtic property, making it particularly useful in the field of finance. Unlike the approach of Romeiro et al., our proposal is also based on scale mixtures of normal distributions (SMN), but with the mixing variable following a BS distribution, resulting in an asymmetric distribution. This new distribution captures leptokurtic character in the distribution, which implies heavier tails and a more pronounced peak compared to BS or StBS distributions (BS based on the Student-t distribution), enabling more realistic modeling of financial data. The resulting multivariate BS-type distribution is an absolutely continuous distribution whose marginal and conditional distributions have leptokurtic properties as compared to the usual univariate BS distribution. These results are a potentially necessary supplement to the recent work of Romeiro et al. This new distribution has not been discussed yet in the literature, and it enriches the family of multivariate BS distributions as it adds new features that take advantage of the presence of observations quite concentrated around the mode. By using the nice hierarchical representation, we have developed a fast and accurate EM (Expectation-Maximization) algorithm for computing the maximum likelihood estimates, and simulation studies show its good performance, and the corresponding asymptotic properties of the estimates. Finally, we illustrate the results with a real dataset, showcasing the effectiveness and practical utility of the proposed distribution.</p>","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"41 6","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/asmb.70053","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145521868","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":"Estimation and Prediction for the Bounded Transformed Gamma Process: A Bayesian Approach","authors":"Massimiliano Giorgio,&nbsp;Fabio Postiglione,&nbsp;Gianpaolo Pulcini","doi":"10.1002/asmb.70054","DOIUrl":"https://doi.org/10.1002/asmb.70054","url":null,"abstract":"<p>In this article, an informative Bayesian approach is proposed for the bounded transformed gamma process, a novel stochastic process recently proposed in the literature to describe bounded above, monotonic increasing, degradation phenomena. The proposed approach is used to analyze a set of real wear data of the cylinder liners of a Diesel engine. Several scenarios, which differ in terms of the quality of the available prior knowledge, are considered and suitable prior distributions are suggested for each of them. In addition, detailed instructions are provided to help potential users incorporate into the suggested prior distributions all and solely the pieces of prior information that are available and sound. In particular, weak prior distributions are also suggested for situations in which available information is poor and/or there is no prior information to exploit. The proposed approach is used to estimate the process parameters and some functions thereof, such as the mean degradation level, the residual reliability of a unit, and to predict the future degradation growth and the useful lifetime. Point estimation and prediction under the (asymmetric) general entropy loss function are also performed to properly deal with situations where overestimation is costlier than underestimation, or vice versa. Estimates and predictions are computed by using proper Markov Chain Monte Carlo algorithms. Results obtained by analyzing wear data of the liners are compared both with those provided by classical methods and with those obtained by using Bayesian approaches based on vague priors. Finally, a sensitivity analysis is developed to study the impact of different prior distributions on the estimates of the parameters.</p>","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"41 6","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/asmb.70054","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145469935","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":"A Comprehensive Framework for Statistical Inference in Measurement System Assessment Studies","authors":"Banafsheh Lashkari,&nbsp;Shojaeddin Chenouri","doi":"10.1002/asmb.70052","DOIUrl":"https://doi.org/10.1002/asmb.70052","url":null,"abstract":"<p>Measurement system analysis aims to quantify the variability in data attributable to the measurement system and evaluate its contribution to overall data variability. This paper conducts a rigorous theoretical investigation of the statistical methods used in such analyses, focusing on variance components and other critical parameters. While established techniques exist for single-variable cases, a systematic theoretical exploration of their properties has been largely overlooked. This study addresses this gap by examining estimators for variance components and other key parameters in measurement system assessment, analyzing their statistical properties, and providing new insights into their reliability, performance, and applicability.</p>","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"41 6","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/asmb.70052","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145469929","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":"Bayesian Change Point Detection via a Generic Sparse Recursive Filter","authors":"Lu Shaochuan","doi":"10.1002/asmb.70056","DOIUrl":"https://doi.org/10.1002/asmb.70056","url":null,"abstract":"<div>\u0000 \u0000 <p>A new sparse recursive filtering is suggested for the efficient inference of the joint posterior of the number of change points and their locations. The computational and storage costs of the sparse recursive filtering are quadratic to the number of uniformisation times generated by the uniformisation scheme, which can be scaled down to the number of change points. This new version of sparse recursive filtering is generally applicable for either conjugate or nonconjugate priors. It is also applicable when either cross-segment dependence or cross-segment independence occurs. Its good performance in some complicated circumstances is demonstrated through examples from robust Bayesian change point detection using <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>t</mi>\u0000 </mrow>\u0000 <annotation>$$ t $$</annotation>\u0000 </semantics></math>-models, Bayesian change point detection with dependence cross-segment, objective Bayesian change point detection and simulation studies, in which the marginal likelihood of the model is often difficult to obtain or intractable.</p>\u0000 </div>","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"41 6","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145469600","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}