Applied Stochastic Models in Business and Industry最新文献_第3页

Dynamic Skewness in Stochastic Volatility Models: A Penalized Prior Approach 随机波动模型中的动态偏度：一种惩罚先验方法

IF 1.5 4区数学

Applied Stochastic Models in Business and Industry Pub Date : 2026-07-01 DOI: 10.1002/asmb.70118

Bruno E. Holtz, Ricardo S. Ehlers, Adriano K. Suzuki, Francisco Louzada

{"title":"Dynamic Skewness in Stochastic Volatility Models: A Penalized Prior Approach","authors":"Bruno E. Holtz, Ricardo S. Ehlers, Adriano K. Suzuki, Francisco Louzada","doi":"10.1002/asmb.70118","DOIUrl":"https://doi.org/10.1002/asmb.70118","url":null,"abstract":"<div>\u0000 \u0000 Financial time series often exhibit skewness and heavy tails, making it essential to use models that incorporate these characteristics to ensure greater reliability in the results. Furthermore, allowing temporal variation in the skewness parameter can bring significant gains in the analysis of this type of series. However, for more robustness, it is crucial to develop models that balance flexibility and parsimony. In this paper, we propose dynamic skewness stochastic volatility models in the scale mixture of skew normal (SMSN) family (DynSSV-SMSN), using priors that penalize model complexity. Parameter estimation is carried out using the Hamiltonian Monte Carlo (HMC) method via the RStan package. Simulation results demonstrated that penalizing priors present superior performance in several scenarios compared to the classical choices. In the empirical application to returns of cryptocurrencies, models with heavy tails and dynamic skewness provided a better fit to the data according to the DIC, WAIC, and LOO-CV information criteria, as well as an improvement in the accuracy of expected shortfall forecasts for Bitcoin applications.\u0000 </div>","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"42 4","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148386369","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

FOREWORD Special Issues: “Explainable Data Science Techniques” 专题：“可解释的数据科学技术”

IF 1.5 4区数学

Applied Stochastic Models in Business and Industry Pub Date : 2026-06-09 DOI: 10.1002/asmb.70115

Rosanna Verde, Paola Cerchiello, Antonella Plaia, Silvia Salini

{"title":"FOREWORD Special Issues: “Explainable Data Science Techniques”","authors":"Rosanna Verde, Paola Cerchiello, Antonella Plaia, Silvia Salini","doi":"10.1002/asmb.70115","DOIUrl":"https://doi.org/10.1002/asmb.70115","url":null,"abstract":"This Special Issue of the ASMBI Journal originated from the conference of the Italian Group for Statistics and Data Science of the Italian Statistical Society, held in Palermo on 11–12 April 2024 (see https://meetings3.sis-statistica.org/index.php/sds2024/). The conference brought together researchers from academia and industry to discuss recent challenges and emerging perspectives in Statistics and Data Science, highlighting the central role of statistical methodologies within the rapidly evolving data science landscape. Particular emphasis was placed on the strong synergy among the diverse scientific disciplines that contribute to the advancement of data science and its applications.While inspired by the main themes addressed during the conference, this Special Issue was intentionally conceived to include a broader spectrum of contributions, encompassing innovative methodological developments and interdisciplinary applications extending beyond the topics specifically covered during the event.The papers collected in this volume highlight the growing convergence between advanced statistical learning, explainable artificial intelligence, and data-driven decision-making across a wide range of application domains. Together, they reflect the increasing need for analytical methodologies that are not only accurate and efficient, but also interpretable, transparent, and capable of supporting responsible and trustworthy AI systems.Several contributions focus on the development of explainable machine learning methodologies. Interpretability and transparency are addressed from multiple perspectives, including causal inference in healthcare analytics in Causal Forests for Discovering Diagnostic Language in Electronic Health Records, interpretable predictive modeling in Extending Explainable Ensemble Trees to Regression Contexts, and explainable approaches for industrial prognosis in Explainable Remaining Useful Life Prediction using Interpretable Divisive Feature Clustering. Explainability is also investigated in cybersecurity applications through Ransomware Detection using Sample Entropy and Graphical Models, while broader ethical and methodological implications are explored in Explainability, Fairness and the Simpson's Paradox in Credit Lending.Another major theme concerns the analysis of complex, heterogeneous, and non-standard data structures. Advanced methodologies for functional and distribution-valued data are proposed in Functional Data Regression on Distribution-Valued Data via Logarithm Derivative Quantile Transformation, while uncertainty-aware learning is addressed in Kernel Principal Component Analysis for Uncertain Data Objects and its Application in Classification. Hybrid probabilistic representations for textual and sentiment analysis are developed in Topic-Sentiment Hybrid Networks for Explainable Document Clustering, highlighting the growing integ","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"42 4","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/asmb.70115","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148208975","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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Correction to ‘Foreword Special Issue on Trustworthy Data Science’ 更正“可信数据科学前言特刊”

IF 1.5 4区数学

Applied Stochastic Models in Business and Industry Pub Date : 2026-06-01 DOI: 10.1002/asmb.70111

引用次数: 0

Semiparametric Analysis of Designed Reliability Improvement Experiments: Termination and Identification 设计可靠性改进实验的半参数分析：终止与识别

IF 1.5 4区数学

Applied Stochastic Models in Business and Industry Pub Date : 2026-05-29 DOI: 10.1002/asmb.70112

Guodong Wang, Huabing Wang, Mengying Shao, Shanshan Lv, Xiangfen Wang, Geoff Vining

{"title":"Semiparametric Analysis of Designed Reliability Improvement Experiments: Termination and Identification","authors":"Guodong Wang, Huabing Wang, Mengying Shao, Shanshan Lv, Xiangfen Wang, Geoff Vining","doi":"10.1002/asmb.70112","DOIUrl":"https://doi.org/10.1002/asmb.70112","url":null,"abstract":"<div>\u0000 \u0000 Reliability practitioners are very interested in which factors affect product reliability. Constrained by time and cost, they hope to terminate the experiment early without affecting the identification results of important factors. In design of experiments (DOE), practitioners usually construct the relationship between parameter and factor effects. However, the true model is usually complicated. Parametric models are often mis-specified. In this article, we proposed a semiparametric framework to terminate the experiment and identify important factors. First, we introduce a loss function to describe the total loss of the manufacturers and the customers, then the response is the type of smaller-the-better. Second, we assume the lifetime data follow a Weibull distribution and get the estimates of parameters using the Bayesian method, then we compute the total loss. Next, we calculate the sum of squares (SS) gotten from different experimental levels and terminate the experiment if all the weighted relative rates of changes of SS are less than a threshold value. Finally, the classification and regression tree (CART) method is introduced to drop some unimportant factors and identify important factors through the analysis of variance (ANOVA). Additionally, we confirm the proposed method through a real example and find that the important factors obtained from the proposed terminated experiment are the same as those found from the original terminated experiment.\u0000 </div>","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"42 3","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148167043","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}

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IF 1.5 4区数学

Applied Stochastic Models in Business and Industry Pub Date : 2026-05-25

引用次数: 0

IF 1.5 4区数学

Applied Stochastic Models in Business and Industry Pub Date : 2026-05-25

引用次数: 0

IF 1.5 4区数学

Applied Stochastic Models in Business and Industry Pub Date : 2026-05-22

引用次数: 0

IF 1.5 4区数学

Applied Stochastic Models in Business and Industry Pub Date : 2026-05-21