Applied Stochastic Models in Business and Industry最新文献

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

Beyond Randomization: Design and Analysis of Discrete Choice Experiments in the Presence of Profile Order Effects Within Choice Sets 超越随机化：在选择集中存在剖面顺序效应的离散选择实验的设计与分析

IF 1.5 4区数学

Applied Stochastic Models in Business and Industry Pub Date : 2025-09-18 DOI: 10.1002/asmb.70043

Yicheng Mao, Roselinde Kessels, Robert Mee

{"title":"Beyond Randomization: Design and Analysis of Discrete Choice Experiments in the Presence of Profile Order Effects Within Choice Sets","authors":"Yicheng Mao, Roselinde Kessels, Robert Mee","doi":"10.1002/asmb.70043","DOIUrl":"https://doi.org/10.1002/asmb.70043","url":null,"abstract":"<p>Discrete Choice Experiments (DCEs) investigate the attributes that affect individual choices among different options and are widely applied across numerous fields. Past DCEs provide clear evidence that the presentation order of the profiles within a choice set can impact the respondents' choices. Ignoring such order effects can produce severely biased estimates, as we illustrate using a product packaging DCE performed for Procter & Gamble in Mexico. Currently, the most common approach to address profile order effects is to randomize the profile order. While this method is relatively easy to implement in online surveys, it can be considerably cumbersome in offline experimental settings. To address this, we suggest incorporating an order covariate in the model to measure the effect of profile order, and propose a Bayesian optimal Balanced Profile Order Design (BPOD) that accounts for this order effect. Our simulation experiments reveal that our Bayesian optimal BPOD achieves accurate parameter estimates comparable to those obtained through randomization in both the multinomial logit model and the panel mixed logit model. Beyond DCEs, this design strategy contributes to broader efforts in experimental design by providing a generalizable framework for addressing structural sources of bias in applied statistical research.</p>","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"41 5","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/asmb.70043","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145101878","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

Forecasting Gold Returns Volatility Over 1258–2023: The Role of Moments 预测1258-2023年黄金收益波动：时刻的作用

IF 1.5 4区数学

Applied Stochastic Models in Business and Industry Pub Date : 2025-09-15 DOI: 10.1002/asmb.70042

Thanoj K. Muddana, Komal S. R. Bhimireddy, Anandamayee Majumdar, Rangan Gupta

{"title":"Forecasting Gold Returns Volatility Over 1258–2023: The Role of Moments","authors":"Thanoj K. Muddana, Komal S. R. Bhimireddy, Anandamayee Majumdar, Rangan Gupta","doi":"10.1002/asmb.70042","DOIUrl":"https://doi.org/10.1002/asmb.70042","url":null,"abstract":"<div>\u0000 \u0000 <p>We analyze the role of leverage, lower and upper tail risks, skewness, and kurtosis of real gold returns in forecasting its volatility over the annual data sample from 1258 to 2023. To conduct our forecasting experiment, we first fit Bayesian time-varying parameters quantile regressions to real gold returns, under six alternative prior settings, to obtain the estimates of volatility (as inter-quantile range), lower and upper tail risks, skewness, and kurtosis. Second, we forecast the derived estimates of conditional volatility using the information contained in leverage of gold returns, tail risks, skewness, and kurtosis using recursively estimated linear predictive regressions over the out-of-sample periods. We find strong statistical evidence of the role of the moments-based predictors in forecasting gold returns volatility over the short to medium term, i.e., till 1–5-year ahead, when compared to the autoregressive benchmark. Robustness of our main result is also validated based on a shorter sample involving higher-frequency data. Our results have important implications for investors and policymakers.</p>\u0000 </div>","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"41 5","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145101132","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

Backdoor Attacks on DNN and GBDT: A Case Study From the Insurance Domain 对DNN和GBDT的后门攻击：来自保险领域的案例研究

IF 1.5 4区数学

Applied Stochastic Models in Business and Industry Pub Date : 2025-09-07 DOI: 10.1002/asmb.70029

Robin Kühlem, Daniel Otten, Daniel Ludwig, Anselm Hudde, Alexander Rosenbaum, Andreas Mauthe

{"title":"Backdoor Attacks on DNN and GBDT: A Case Study From the Insurance Domain","authors":"Robin Kühlem, Daniel Otten, Daniel Ludwig, Anselm Hudde, Alexander Rosenbaum, Andreas Mauthe","doi":"10.1002/asmb.70029","DOIUrl":"https://doi.org/10.1002/asmb.70029","url":null,"abstract":"<p>Machine learning (ML) will most likely play a large role in many processes in the future, also in the insurance industry. However, ML models are at risk of being attacked and manipulated. A model compromised by a backdoor attack loses its integrity and can no longer be deemed trustworthy. Ensuring the trustworthiness of ML models is crucial, as compromised models can lead to significant financial and reputational damage for insurance companies. In this work the robustness of Gradient Boosted Decision Tree (GBDT) models and Deep Neural Networks (DNN) within an insurance context is evaluated. Therefore, two GBDT models and two DNNs are trained on two different tabular datasets from an insurance context. Past research in this domain mainly used homogeneous data and there are comparably little insights regarding heterogeneous tabular data. The ML tasks performed on the datasets are claim prediction (regression) and fraud detection (binary classification). For the backdoor attacks different samples containing a specific pattern were crafted and added to the training data. It is shown, that this type of attack can be highly successful, even with a few added samples. The backdoor attacks worked well on the models trained on one dataset but poorly on the models trained on the other. In real-world scenarios the attacker will have to face several obstacles but as attacks can work with very few added samples this risk should be evaluated. Therefore, understanding and mitigating these risks is essential for the reliable deployment of ML in critical applications.</p>","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"41 5","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/asmb.70029","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145007920","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

Pricing Basket Spread Option Under the Correlated Skew Brownian Motions 相关偏布朗运动下的一篮子价差期权定价

IF 1.5 4区数学

Applied Stochastic Models in Business and Industry Pub Date : 2025-08-31 DOI: 10.1002/asmb.70040

Qifeng Zhong, Xingye Yue, Jing Yao

引用次数: 0

Directional False Discovery Rate Control in Large-Scale Multiple Testing Under Data Dependence 数据依赖下大规模多重测试的定向错误发现率控制

IF 1.5 4区数学

Applied Stochastic Models in Business and Industry Pub Date : 2025-08-31 DOI: 10.1002/asmb.70041

Wendong Li, Jianqing Shi, Yi Wang, Dongdong Xiang

{"title":"Directional False Discovery Rate Control in Large-Scale Multiple Testing Under Data Dependence","authors":"Wendong Li, Jianqing Shi, Yi Wang, Dongdong Xiang","doi":"10.1002/asmb.70041","DOIUrl":"https://doi.org/10.1002/asmb.70041","url":null,"abstract":"<div>\u0000 \u0000 <p>Detecting directional signals in multiple testing is crucial to take targeted and effective measures. In this article, we consider the directional multiple testing under the dependence problem within a three-group model. Given the assumption that the observed data are generated according to an underlying three-state hidden Markov model, we develop oracle and data-driven procedures to maximize the expected number of true discoveries (ETD) while controlling the false discovery rates (FDRs) of both alternative states at their nominal levels. It is shown theoretically that the proposed directional multiple testing procedures are valid and have certain optimality properties for directional FDR-control. An extensive numerical study shows that our procedures are significantly more powerful than their competitors since the former can accommodate the dependence structure among hypotheses. The proposed procedures also exhibit high flexibility by allowing different nominal levels for the two alternative states, which is appealing in cases when the false discoveries of different alternative states are not equally important. As a demonstration, the proposed data-driven procedure is applied to learn the transcriptomic characteristics of bronchoalveolar lavage fluid in COVID-19 patients.</p>\u0000 </div>","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"41 5","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144923789","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

Bayesian Analysis of Shared Frailty Models for Repairable Systems Subject to Imperfect Repair 不完全修复下可修复系统共享脆弱性模型的贝叶斯分析

IF 1.5 4区数学

Applied Stochastic Models in Business and Industry Pub Date : 2025-08-28 DOI: 10.1002/asmb.70039

Éder S. Brito, Vera L. D. Tomazella, Paulo H. Ferreira, Francisco Louzada

{"title":"Bayesian Analysis of Shared Frailty Models for Repairable Systems Subject to Imperfect Repair","authors":"Éder S. Brito, Vera L. D. Tomazella, Paulo H. Ferreira, Francisco Louzada","doi":"10.1002/asmb.70039","DOIUrl":"https://doi.org/10.1002/asmb.70039","url":null,"abstract":"<div>\u0000 \u0000 <p>Repairable systems, crucial in reliability studies, are characterized by recurrent failure times modeled as counting processes with intensity functions. This paper explores models for these failure times incorporating imperfect repairs, addressing unobserved heterogeneity via shared frailty models. In this context, our approach involves scenarios with general imperfect repairs, which offer a more realistic perspective compared to the minimal or perfect repair assumptions commonly employed in the reliability literature. We propose hierarchical Bayesian methods to estimate parameters, leveraging the Power-Law Process for initial intensities and gamma distributions for frailty terms. Bayesian methods are highly flexible and can accommodate complex shared frailty models that include random effects and dependencies between units. Applying Bayesian inference with gamma and beta distribution priors, coupled with Monte Carlo simulations, provides a robust methodology for estimating unknown parameters and deriving posterior distributions. This flexibility is crucial for capturing the underlying structure of the data in repairable systems with imperfect repairs. Our hierarchical Bayesian framework accommodates multiple systems, providing insights into failure processes and supporting enhanced maintenance strategies. We demonstrate our approach using a real failure times dataset and evaluate its performance through simulation studies, showcasing its applicability and relevance in practical settings.</p>\u0000 </div>","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"41 5","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144915101","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

Inference for Simple Step Stress Accelerated Life Test Model Under Progressively Censored Gompertz Data 渐进式截尾Gompertz数据下简单阶跃应力加速寿命试验模型的推断

IF 1.5 4区数学

Applied Stochastic Models in Business and Industry Pub Date : 2025-08-25 DOI: 10.1002/asmb.70037

Rajat Das, Yogesh Mani Tripathi, Liang Wang, Shuo-Jye Wu

引用次数: 0