Applied Stochastic Models in Business and Industry最新文献

{"title":"Optimal Exact Designs of Multiresponse Experiments Under Linear and Sparsity Constraints","authors":"Lenka Filová,&nbsp;Pál Somogyi,&nbsp;Radoslav Harman","doi":"10.1002/asmb.70072","DOIUrl":"https://doi.org/10.1002/asmb.70072","url":null,"abstract":"<div>\u0000 \u0000 <p>We propose a computational approach to constructing exact designs on finite design spaces that are optimal for multiresponse regression experiments under a combination of the standard linear and specific ‘sparsity’ constraints. The linear constraints address, for example, limits on multiple resource consumption and the problem of optimal design augmentation, while the sparsity constraints control the set of distinct trial conditions utilized by the design. The key idea is to construct an artificial optimal design problem that can be solved using any existing mathematical programming technique for univariate-response optimal designs under pure linear constraints. The solution to this artificial problem can then be directly converted into an optimal design for the primary multivariate-response setting with combined linear and sparsity constraints. We demonstrate the utility and flexibility of the approach through a dose-response case study with multivariate responses that sequentially adds constraints on safety, efficacy, and cost, where cost also depends on the number of distinct doses used.</p>\u0000 </div>","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"42 2","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146176135","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":"Robust Planning of Accelerated Destructive Degradation Tests: Model Discrimination, Parameter Estimation, and Lifetime Prediction","authors":"Lin Wu,&nbsp;Xiao-Dong Zhou,&nbsp;Rong-Xian Yue","doi":"10.1002/asmb.70070","DOIUrl":"https://doi.org/10.1002/asmb.70070","url":null,"abstract":"<div>\u0000 \u0000 <p>Accelerated destructive degradation tests (ADDTs) play a cr\u0000ucial role in providing timely reliability information for long-life products. Previous research has primarily focused on identifying optimal ADDT plans for accurately predicting specific quantiles of the failure-time distribution under use conditions, utilizing a given degradation model. However, a common challenge during the experimental design phase is the lack of confidence in selecting a model that accurately represents the underlying data. In this paper, we introduce a novel approach that specifically addresses optimal ADDT planning for model discrimination. We propose KL-optimal design criteria to enhance the model discrimination capability of ADDTs. Moreover, we present compound DKL- and CKL-optimal design criteria to ensure that ADDT plans can simultaneously achieve effective model discrimination alongside accurate parameter estimation or precise quantile prediction. We establish equivalence theorems to validate the optimality of the proposed designs. Meanwhile, we employ the particle swarm optimization algorithm to efficiently compute optimal ADDT plans. Through a practical application and sensitivity analysis, we demonstrate the effectiveness and robustness of our optimal designs. Our proposed method offers engineers with a valuable solution for developing optimal ADDT plans that meet multiple objectives.</p>\u0000 </div>","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"42 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146176658","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":"Three-Way Data Analysis With Explainable Tucker3 Clustering (XT3Clus)","authors":"Mariaelena Bottazzi Schenone,&nbsp;Tiziano Iannaccio,&nbsp;Ilaria Mozzetta,&nbsp;Maurizio Vichi","doi":"10.1002/asmb.70069","DOIUrl":"https://doi.org/10.1002/asmb.70069","url":null,"abstract":"<p>In an era of increasingly complex data, three-way arrays capturing information across units, variables and occasions are ubiquitous in fields from chemometrics to finance. However, extracting meaningful and interpretable patterns from such data remain a significant challenge. To address this, we introduce the Explainable Tucker3 Clustering (XT3Clus) methodology. XT3Clus performs clustering on units while simultaneously identifying explainable components for variables and/or occasions, significantly enhancing model interpretability. This approach functions as a constrained Tucker3 model, where each dimension is forced to contribute to a single component. The framework supports fully confirmatory, exploratory or hybrid analytical strategies. The optimization of the objective function is carried out by an efficient Alternating Least Squares algorithm. Finally, we propose a novel quantitative metric to evaluate the interpretability of a solution and confirm the practical utility of XT3Clus in three real-world scenarios.</p>","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"42 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/asmb.70069","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146049421","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":"Multi-Objective Redundancy Allocation Problem for Systems With Weighted \u0000 \u0000 \u0000 k\u0000 \u0000 $$ k $$\u0000 -Out-of-\u0000 \u0000 \u0000 n\u0000 \u0000 $$ n $$\u0000 Subsystems Formed by Different Types of Multistate Components","authors":"Darshana Yadav,&nbsp;Mithu Rani Kuiti,&nbsp;Maxim Finkelstein","doi":"10.1002/asmb.70068","DOIUrl":"https://doi.org/10.1002/asmb.70068","url":null,"abstract":"<div>\u0000 \u0000 <p>The redundancy allocation problem (RAP) is considered as one of the important problems in reliability theory. In this paper, we consider a series system with several subsystems wherein each subsystem is a weighted <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>k</mi>\u0000 </mrow>\u0000 <annotation>$$ k $$</annotation>\u0000 </semantics></math>-out-of-<span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>n</mi>\u0000 </mrow>\u0000 <annotation>$$ n $$</annotation>\u0000 </semantics></math> system formed by different types of multi-state components. The degradation of the performance level (i.e., the probability of changing from a given state <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>i</mi>\u0000 </mrow>\u0000 <annotation>$$ i $$</annotation>\u0000 </semantics></math> to the next state <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mo>(</mo>\u0000 <mi>i</mi>\u0000 <mo>−</mo>\u0000 <mn>1</mn>\u0000 <mo>)</mo>\u0000 </mrow>\u0000 <annotation>$$ left(i-1right) $$</annotation>\u0000 </semantics></math>) of a component of the system is modeled by the Markov process. Then, we study the multi-objective RAP problem for this system, that is, we determine the optimum number of components of each type in each subsystem so that the maximum system reliability is achieved at minimum cost. Note that the given RAP problem is of NP-hard type, and consequently, we use the controlled elitism non-dominated ranked genetic algorithm (CE-NRGA) to solve this problem. At the end, we illustrate the proposed methodology through a numerical example. Moreover, we discuss a case study to validate the proposed model.</p>\u0000 </div>","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"42 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145964232","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":"The Valuation of Mortgages With Partial Prepayment Risk Under the Equal Principal Payment Method","authors":"Congjin Zhou,&nbsp;Yinghui Dong","doi":"10.1002/asmb.70066","DOIUrl":"https://doi.org/10.1002/asmb.70066","url":null,"abstract":"<div>\u0000 \u0000 <p>This article develops a valuation model for mortgages with partial prepayment risk under the equal principal payment method. To model partial prepayment, we assume prepayment events occur at the jump times of a point process. The proportion of the prepayment amount to the outstanding principal balance is characterized by a stochastic process. By defining specific forms for the point process and stochastic process, we derive explicit valuation formulas for the mortgage loan. Finally, after resolving the computational challenge of multiple integrals via matrix exponentiation techniques, numerical results are presented to investigate the impact of some parameters on the valuation.</p>\u0000 </div>","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"42 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145887824","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":"Optimal Transport Autoregression to Forecast High-Frequency Financial Data Distributions","authors":"Paolo Pagnottoni","doi":"10.1002/asmb.70067","DOIUrl":"https://doi.org/10.1002/asmb.70067","url":null,"abstract":"<p>In this paper, we study the properties and performance of optimal transport autoregression in modeling and forecasting high-frequency financial data distributions. We build on a class of univariate autoregressive transport models recently proposed in the literature (Zhu and Müller) where the distributional time series dynamics is modeled either through a single scalar, similarly with traditional Euclidean autoregressive models, or via a functional distribution-contraction coefficient. Properties and performance of the models are investigated through an empirical application to forecast distributions of high-frequency financial price returns and volatility of Bitcoin. Our results show that forecast errors are highly time- and quantile-dependent: while autoregressive transport models are generally able to predict return and volatility densities during “normal business” periods, forecast errors tend to rise in the proximity of extreme quantiles, though such increase is non-monotonic. We highlight the strengths and weaknesses of the method in modeling the distributional time series of high-frequency, noisy financial data, suggesting some potential directions for future research.</p>","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"42 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/asmb.70067","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145905216","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":"Measuring and Assessing the Healthcare Services Experience: A Proposal of a Synthetic Index","authors":"Leonardo Salvatore Alaimo,&nbsp;Filomena Maggino","doi":"10.1002/asmb.70065","DOIUrl":"https://doi.org/10.1002/asmb.70065","url":null,"abstract":"<div>\u0000 \u0000 <p>The evaluation of healthcare services is a crucial aspect of public health management, as it provides insights into service effectiveness, efficiency, and user satisfaction. This paper proposes a multidimensional approach to measuring healthcare service experience by constructing a synthetic index that incorporates three key perceptual dimensions: Cost, accessibility, and quality. These latent constructs are measured using a set of elementary indicators from the European Quality of Life Survey. To develop the dimensional synthetic indices—one for each experience dimension—as well as the overall experience index and the customer satisfaction index, we employ Partial Least Squares Path Modeling (PLS-PM). This approach not only enables the synthesis of latent variables but also allows for the analysis of structural relationships between them. The results provide a comprehensive framework for assessing healthcare service experiences and offer valuable insights for policymakers and service providers aiming to enhance healthcare quality and accessibility while improving user satisfaction.</p>\u0000 </div>","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"42 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145891154","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":"Extending Explainable Ensemble Trees to Regression Contexts","authors":"Massimo Aria,&nbsp;Agostino Gnasso,&nbsp;Carmela Iorio,&nbsp;Marjolein Fokkema","doi":"10.1002/asmb.70064","DOIUrl":"https://doi.org/10.1002/asmb.70064","url":null,"abstract":"<div>\u0000 \u0000 <p>The advent of ensemble methods, such as Random Forest (RF), has led to a paradigm shift in supervised learning. These methods have achieved remarkable levels of prediction accuracy by aggregating multiple weak learners. However, a drawback of these methods is their lack of transparency, which often prevents users from understanding their prediction processes. In light of these challenges, Explainable Ensemble Trees (E2Tree) has recently been proposed, providing a graphical representation of the relationships between response variables and predictors in RFs for classification. E2Tree constructs a single decision tree based on (dis)similarities between observations. By summarizing both distances in terms of predictors and a forest as a single decision tree, E2Tree merges the strengths of both decision trees and decision tree ensembles. In this paper, we propose to extend the E2Tree methodology to regression contexts. We investigate the performance of E2Tree for regression using real-world datasets. We use the Mantel test to test the correlation between similarities of the RF and E2Tree.</p>\u0000 </div>","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"42 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145887585","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":"GARCH With Intervention Analysis to Evaluate Short Selling Restrictions","authors":"Wilson Calmon,&nbsp;Gabriel Mizuno,&nbsp;Sara Paixão,&nbsp;Adrian Pizzinga","doi":"10.1002/asmb.70063","DOIUrl":"https://doi.org/10.1002/asmb.70063","url":null,"abstract":"<div>\u0000 \u0000 <p>At a critical moment in the 2007–2009 financial crisis, financial authorities in the US, Japan, the United Kingdom, France, Canada, and Germany unanimously banned short sales in their respective markets. We estimate GARCH models with intervention analysis to assess the effect of such regulatory decisions on the <i>unconditional</i> or <i>long-term</i> stock market volatility, and we focus on trading days under short selling restrictions. Contrary to the conclusion reached by some important literature, our findings reveal that, for all six aforementioned markets, the volatility did not grow once the restrictions were imposed, and it began to decrease to the levels observed previously to the bankruptcy of Lehman Brothers.</p>\u0000 </div>","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"42 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145891543","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":"Kernel Principal Component Analysis for Uncertain Data Objects and Its Application in Classification","authors":"Changwan Ko,&nbsp;Behnam Tavakkol,&nbsp;Youngseon Jeong","doi":"10.1002/asmb.70062","DOIUrl":"https://doi.org/10.1002/asmb.70062","url":null,"abstract":"<div>\u0000 \u0000 <p>Uncertain data mining has been a growing field of research in recent years. Numerous data mining techniques for performing tasks such as clustering, classification, anomaly detection, and so on have been developed for uncertain data. Principal component analysis (PCA) and its extension, kernel principal component analysis (KPCA), are two well-known techniques that are widely used for dimensionality reduction and feature extraction for traditional certain data. However, for uncertain data, these techniques have not been developed to the best of our knowledge. In this paper, uncertain principal component analysis (UPCA) and uncertain kernel principal component analysis (UKPCA) are developed. The proposed techniques consider the inherent uncertainty of the uncertain data, unlike the traditional techniques that ignore such uncertainty. In addition, in this paper, we propose the decision tree algorithm classification model combined with the developed UKPCA technique. The proposed model is capable of achieving high classification accuracy for both real-world and synthetic data, especially for cases that involve classes having nonlinear/arbitrary shapes.</p>\u0000 </div>","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"42 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145824845","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}