Applied Stochastic Models in Business and Industry最新文献_第8页

A Comprehensive Framework for Statistical Inference in Measurement System Assessment Studies 测量系统评估研究中统计推断的综合框架

IF 1.5 4区数学

Applied Stochastic Models in Business and Industry Pub Date : 2025-11-05 DOI: 10.1002/asmb.70052

Banafsheh Lashkari, Shojaeddin Chenouri

引用次数: 0

Bayesian Change Point Detection via a Generic Sparse Recursive Filter 基于通用稀疏递归滤波器的贝叶斯变化点检测

IF 1.5 4区数学

Applied Stochastic Models in Business and Industry Pub Date : 2025-11-04 DOI: 10.1002/asmb.70056

Lu Shaochuan

{"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}

引用次数: 0

A Class of Shock Models for a System That is Equipped With a Protection Block With an Application to Wind Turbine Reliability 一类带保护块系统的冲击模型及其在风力机可靠性研究中的应用

IF 1.5 4区数学

Applied Stochastic Models in Business and Industry Pub Date : 2025-11-02 DOI: 10.1002/asmb.70051

Serkan Eryilmaz

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Copula-Based Pairs Trading on Brazilian Stock Exchange Equities Copula-Based配对交易在巴西证券交易所股票

IF 1.5 4区数学

Applied Stochastic Models in Business and Industry Pub Date : 2025-11-02 DOI: 10.1002/asmb.70049

Daniel Henrique Salgado, Osvaldo Candido

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Explainable Fairness and Propensity Score Matching 可解释公平与倾向得分匹配

IF 1.5 4区数学

Applied Stochastic Models in Business and Industry Pub Date : 2025-10-28 DOI: 10.1002/asmb.70047

Paolo Giudici, Golnoosh Babaei

{"title":"Explainable Fairness and Propensity Score Matching","authors":"Paolo Giudici, Golnoosh Babaei","doi":"10.1002/asmb.70047","DOIUrl":"https://doi.org/10.1002/asmb.70047","url":null,"abstract":"<p>Fairness is a key requirement for artificial intelligence applications. The assessment of fairness is typically based on group-based measures, such as statistical parity, which compares the machine learning output for the different population groups of a protected variable. Although intuitive and simple, statistical parity may be affected by the presence of control variables, correlated with the protected variable. To remove this effect, we propose to employ Shapley values, which measure the additional difference in output specifically due to the protected variable. To remove the possible impact of correlations on Shapley values, we compare them across different subgroups of the most correlated control variables, checking for the presence of Simpson's paradox, for which a fair model may become unfair when conditioning on a control variable. We also show how to mitigate unfairness by means of a propensity score matching that can improve statistical parity, building a training sample that matches similar individuals in different protected groups. We apply our proposal to a real-world database containing 157,269 personal lending decisions and show that both logistic regression and random forest models are fair when all loan applications are considered, but become unfair for high loan amounts requested. We show how propensity score matching can mitigate this bias.</p>","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"41 6","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/asmb.70047","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145406708","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}

引用次数: 0

Inference on Common Intraday Periodicity at High Frequencies With Jumps 有跳跃的高频共日内周期的推论

IF 1.5 4区数学

Applied Stochastic Models in Business and Industry Pub Date : 2025-10-27 DOI: 10.1002/asmb.70050

Fan Wu

引用次数: 0

Assessing the Impact on Sales of Competing Nearby Supermarkets 评估对附近竞争超市销售的影响

IF 1.5 4区数学

Applied Stochastic Models in Business and Industry Pub Date : 2025-10-22 DOI: 10.1002/asmb.70048

Daniel González Ibáñez, Xavier Puig

{"title":"Assessing the Impact on Sales of Competing Nearby Supermarkets","authors":"Daniel González Ibáñez, Xavier Puig","doi":"10.1002/asmb.70048","DOIUrl":"https://doi.org/10.1002/asmb.70048","url":null,"abstract":"<p>The supermarket industry faces fierce competition in many geographic areas, with various brands competing for a market share that represents a significant portion of a country's GDP. For these businesses, understanding the factors that influence their sales and how they do so is crucial: location, pricing, quality, customer loyalty, and more. While much has been written about how these elements can affect revenue, there is a lack of existing models quantifying the monetary impact of having competitor stores within an own establishment area of influence. This article introduces a hierarchical Bayesian model that quantifies this impact for each product category within the brand's catalog. The model requires careful parameterization to adapt to the specific context, and therefore both its application and design represent a methodological novelty in this area. The main result is a matrix of coefficients that indicate the monetary effect of the presence of every competitor for each product category. The findings revealed by the model show that the influence of the proximity of each competitor does indeed vary depending on these categories. This information is valuable for the company when making decisions, such as choosing new store locations or determining the product assortment to offer in each establishment. The use of the results derived from this model provides a competitive advantage over others and helps to better understand the market by identifying which supermarket brands are the perceived leaders in each product category.</p>","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"41 6","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/asmb.70048","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145366657","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}

引用次数: 0

Scalable Inference via Averaged Robbins-Monro Bootstrap 基于平均罗宾斯-门罗Bootstrap的可扩展推理

IF 1.5 4区数学

Applied Stochastic Models in Business and Industry Pub Date : 2025-10-14 DOI: 10.1002/asmb.70046

Giuseppe Alfonzetti, Ruggero Bellio

引用次数: 0

Conformal Prediction Inference in Regularized Insurance Models 正则化保险模型的保形预测推理

IF 1.5 4区数学

Applied Stochastic Models in Business and Industry Pub Date : 2025-10-12 DOI: 10.1002/asmb.70045

Alokesh Manna, Aditya Vikram Sett, Dipak K. Dey, Yuwen Gu, Elizabeth D. Schifano, Jichao He

{"title":"Conformal Prediction Inference in Regularized Insurance Models","authors":"Alokesh Manna, Aditya Vikram Sett, Dipak K. Dey, Yuwen Gu, Elizabeth D. Schifano, Jichao He","doi":"10.1002/asmb.70045","DOIUrl":"https://doi.org/10.1002/asmb.70045","url":null,"abstract":"<div>\u0000 \u0000 <p>Prediction uncertainty quantification has become a key research topic in recent years, with applications in both scientific and business problems. In the insurance industry, assessing the range of possible claim costs for individual drivers improves premium pricing accuracy. It also enables insurers to manage risk more effectively by accounting for uncertainty in accident likelihood and severity. In the presence of covariates, a variety of regression-type models are often used for modeling insurance claims, ranging from relatively simple generalized linear models (GLMs) to regularized GLMs to gradient boosting models (GBMs). Conformal predictive inference has arisen as a popular distribution-free approach for quantifying predictive uncertainty under relatively weak assumptions of exchangeability, and has been well studied under the classic linear regression setting. In this work, we leverage GLMs and GBMs to define meaningful non-conformity measures, which are then used within the conformal prediction framework to provide reliable uncertainty quantification for these types of regression problems. Using regularized Tweedie GLM regression and LightGBM with Tweedie loss, we demonstrate conformal prediction performance with these non-conformity measures in insurance claims data. Our simulation results favor the use of locally weighted Pearson residuals for LightGBM over other methods considered, as the resulting intervals maintained the nominal coverage with the smallest average width.</p>\u0000 </div>","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"41 5","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145316806","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

Evaluating Uncertainties in Health Economic Models: A Review and Guide 评估卫生经济模型中的不确定性：综述与指南

IF 1.5 4区数学

Applied Stochastic Models in Business and Industry Pub Date : 2025-10-02 DOI: 10.1002/asmb.70044

Mohammad A. Chaudhary, Haitao Chu, Joseph C. Cappelleri

{"title":"Evaluating Uncertainties in Health Economic Models: A Review and Guide","authors":"Mohammad A. Chaudhary, Haitao Chu, Joseph C. Cappelleri","doi":"10.1002/asmb.70044","DOIUrl":"https://doi.org/10.1002/asmb.70044","url":null,"abstract":"<div>\u0000 \u0000 <p>In health economics, decision-makers rely on models to assess the cost-effectiveness of healthcare interventions and guide resource allocation. Health Technology Assessment (HTA) agencies employ cost-effectiveness models to determine the approval and market access of new therapies within their respective jurisdictions. Health economists use quantitative techniques to synthesize clinical, epidemiological, and economic data to model the costs and effectiveness of a new drug compared to the current standard of care over the lifetime of the patients. These models frequently integrate a wide range of assumptions and data inputs from various sources, which renders them vulnerable to a significant level of uncertainty. Economic models commonly confront multiple forms of uncertainty, such as stochastic uncertainty (first-order), which differs from parameter uncertainty (second-order), as well as the presence of heterogeneity within patient populations. Additionally, structural uncertainty related to the model itself adds another layer of complexity. Uncertainty assessment is essential in model-based health economic evaluations that inform regulatory and reimbursement decisions. Understanding these sources of uncertainty, taking steps to minimize their impact, and analyzing, quantifying, and reporting these inherent uncertainties are crucial for ensuring that health economic models provide robust and reliable insights for effective decision-making. This article examines different types of uncertainty in health economic models and methods to analyze and quantify them, offering practical guidelines with examples from recent literature.</p>\u0000 </div>","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"41 5","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145224113","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