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

On Classical Inference of a Flexible Semi-Parametric Class of Distributions Under a Joint Balanced Progressive Censoring Scheme

IF 1.3 4区数学

Applied Stochastic Models in Business and Industry Pub Date : 2025-01-30 DOI: 10.1002/asmb.2924

Dhrubasish Bhattacharyya, Debasis Kundu

引用次数: 0

Enhancing Predictive Modeling of Chinese Yam Shape Through Bayesian Linear Modeling and Key Diameter Modification

IF 1.3 4区数学

Applied Stochastic Models in Business and Industry Pub Date : 2025-01-30 DOI: 10.1002/asmb.2921

Haifeng Zhang, Koki Kyo, Mitsuru Hachiya, Hideo Noda

{"title":"Enhancing Predictive Modeling of Chinese Yam Shape Through Bayesian Linear Modeling and Key Diameter Modification","authors":"Haifeng Zhang, Koki Kyo, Mitsuru Hachiya, Hideo Noda","doi":"10.1002/asmb.2921","DOIUrl":"https://doi.org/10.1002/asmb.2921","url":null,"abstract":"<p>In the development of devices for cutting Chinese yams into chunks for use as seeds, accurately measuring the yam's shape with a simple mechanism is crucial. In our prior study, we introduced a statistical approach for predicting the shape of a Chinese yam based on its key diameters. This method involves organizing sample data, estimating diameters at discrete points along the central axis, and constructing a predictive model based on these estimated diameters. However, the initial predictive model relied on separate regression models for each point, potentially leading to instability. In this article, we enhance our previous approach by incorporating a new step that refines the estimation of regression coefficients through Bayesian linear modeling methods. This modification allows for the simultaneous estimation of regression coefficients, ensuring greater stability in the reconstructed model. Additionally, we modify the method for locating key diameters. To validate the performance of the enhanced approach, we apply it to a set of samples and compare the output of the reconstructed model with that of our initial method. The results demonstrate improved stability and performance, highlighting the efficacy of the refined modeling technique.</p>","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"41 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/asmb.2921","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143121025","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

Latent Activation Limited Failure Models, Stochastic Ordering and Identifiability

IF 1.3 4区数学

Applied Stochastic Models in Business and Industry Pub Date : 2025-01-30 DOI: 10.1002/asmb.2920

Qi Jiang, Sanjib Basu

引用次数: 0

Interpretable Machine Learning Based on Functional ANOVA Framework: Algorithms and Comparisons

IF 1.3 4区数学

Applied Stochastic Models in Business and Industry Pub Date : 2025-01-29 DOI: 10.1002/asmb.2916

Linwei Hu, Vijayan N. Nair, Agus Sudjianto, Aijun Zhang, Jie Chen, Zebin Yang

{"title":"Interpretable Machine Learning Based on Functional ANOVA Framework: Algorithms and Comparisons","authors":"Linwei Hu, Vijayan N. Nair, Agus Sudjianto, Aijun Zhang, Jie Chen, Zebin Yang","doi":"10.1002/asmb.2916","DOIUrl":"https://doi.org/10.1002/asmb.2916","url":null,"abstract":"<div>\u0000 \u0000 <p>In the early days of machine learning (ML), the emphasis was on developing complex algorithms to achieve best possible predictive performance. To understand and explain the model results, one had to rely on post hoc explainability techniques, which are known to have limitations. Recently, with the recognition in regulated industries that interpretability is also important, researchers are studying algorithms that compromise on small increases in predictive performance in favor of being more interpretable. While doing so, the ML community has rediscovered the use of low-order functional ANOVA (fANOVA) models that have been known in the statistical literature for some time. This paper starts with a description of challenges with post hoc explainability. This is followed by a brief review of the fANOVA framework with a focus on models with just main effects and second-order interactions (called generalized additive models with interactions or GAMI = GAM + Interactions). It then provides an overview of two recently developed GAMI techniques: Explainable Boosting Machines or EBM and GAMI-Net. The paper proposes a new algorithm that also uses trees, as in EBM, but does linear fits instead of piecewise constants within the partitions. We refer to this as GAMI-linear-tree (GAMI-Lin-T). There are many other differences, including the development of a new interaction filtering algorithm. The paper uses simulated and real datasets to compare the three fANOVA ML algorithms. The results show that GAMI-Lin-T and GAMI-Net have comparable performances, and both are generally better than EBM.</p>\u0000 </div>","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"41 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143120641","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

Negative Probability

IF 1.3 4区数学

Applied Stochastic Models in Business and Industry Pub Date : 2025-01-29 DOI: 10.1002/asmb.2910

Nick Polson, Vadim Sokolov

引用次数: 0

Heterogeneity in Populations and the Paradoxes of Survival: A Tribute to Nozer Singpurwalla

IF 1.3 4区数学

Applied Stochastic Models in Business and Industry Pub Date : 2025-01-29 DOI: 10.1002/asmb.2919

Maxim Finkelstein, Ji Hwan Cha

引用次数: 0

Probability and Fuzzy Working in Concert—Honoring the Reliability Contributions of Nozer D. Singpurwalla

IF 1.3 4区数学

Applied Stochastic Models in Business and Industry Pub Date : 2025-01-29 DOI: 10.1002/asmb.2918

Kimberly F. Sellers, Jane M. Booker

{"title":"Probability and Fuzzy Working in Concert—Honoring the Reliability Contributions of Nozer D. Singpurwalla","authors":"Kimberly F. Sellers, Jane M. Booker","doi":"10.1002/asmb.2918","DOIUrl":"https://doi.org/10.1002/asmb.2918","url":null,"abstract":"<div>\u0000 \u0000 <p>Since Lotfi Zadeh introduced fuzzy logic and fuzzy sets, this theory characterizing the uncertainty of classification has a proven record in fields of computation and engineering. These successful applications, however, have been falsely interpreted as competition or replacement of probability theory by those in many statistical and mathematical communities. Such misconceptions are the result of a lack of understanding about types of uncertainties, and anchored attitudes clinging to the past. Nozer Singpurwalla, among other statisticians, came to the realization that probability and fuzzy set theory can and should work in concert (i.e., not in competition) to accommodate two different types of uncertainty present within a problem or system. The authors had the honor to collaborate with Nozer; those works are featured as successful applications of the probability measure of fuzzy sets in reliability where respective uncertainties of the outcome of events and of classification exist. This paper features those works which embody the use of Bayesian analysis and the subjective interpretation of probability.</p>\u0000 </div>","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"41 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143120640","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 to the Special Issue on Energy Finance and Climate Change 能源金融与气候变化特刊前言

IF 1.3 4区数学

Applied Stochastic Models in Business and Industry Pub Date : 2024-12-03 DOI: 10.1002/asmb.2909

Roberto Baviera, Carlo Sgarra, Tiziano Vargiolu, Rituparna Sen

引用次数: 0

Is (Independent) Subordination Relevant in Equity Derivatives? 独立）从属关系与股票衍生品相关吗？

IF 1.4 4区数学

Applied Stochastic Models in Business and Industry Pub Date : 2024-11-18 DOI: 10.1002/asmb.2904

Michele Azzone, Roberto Baviera

引用次数: 0

Renewable Energy Investments, Support Schemes and the Dirty Option 可再生能源投资，支持计划和肮脏的选择