Applied Stochastic Models in Business and Industry最新文献

{"title":"Topic-Sentiment Hybrid Networks for Explainable Document Clustering: A Probabilistic Multi-Dimensional Similarity Analysis","authors":"Marco Ortu","doi":"10.1002/asmb.70024","DOIUrl":"https://doi.org/10.1002/asmb.70024","url":null,"abstract":"<p>This study introduces a statistical methodology for document clustering that integrates multiple dimensions of textual similarity through network topology analysis. The proposed methodology, which we call Multi-dimensional Similarity Network Analysis (MSNA), extends traditional document-clustering approaches by combining semantic embeddings, topic probability distributions, and emotional probability distribution into a unified similarity measure. We formalize this through a weighted combination of Jensen-Shannon divergences across different probability spaces, creating a comprehensive similarity network. The clustering is achieved through a community detection algorithm that optimizes a multi-objective modularity function, accounting for the different similarity dimensions. We prove the statistical consistency of our approach and derive bounds for the clustering performance under mild regularity conditions. The methodology is validated on a large-scale data set of Airbnb reviews <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mo>(</mo>\u0000 <mi>n</mi>\u0000 <mo>=</mo>\u0000 <mn>114</mn>\u0000 <mo>,</mo>\u0000 <mn>000</mn>\u0000 <mo>)</mo>\u0000 </mrow>\u0000 <annotation>$$ left(n=114,000right) $$</annotation>\u0000 </semantics></math> from Sardinia, Italy, containing text content, topic distributions, and emotional features. Results show significant improvements in both clustering quality (average silhouette score increased) and interpretability compared to traditional single-dimension approaches. From an empirical perspective, the synthetic data validation demonstrates robust performance with topic strength in the range <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mo>[</mo>\u0000 <mn>0</mn>\u0000 <mo>.</mo>\u0000 <mn>4</mn>\u0000 <mo>,</mo>\u0000 <mn>1</mn>\u0000 <mo>.</mo>\u0000 <mn>0</mn>\u0000 <mo>]</mo>\u0000 </mrow>\u0000 <annotation>$$ left[0.4,1.0right] $$</annotation>\u0000 </semantics></math> and emotion strength in <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mo>[</mo>\u0000 <mn>0</mn>\u0000 <mo>.</mo>\u0000 <mn>2</mn>\u0000 <mo>,</mo>\u0000 <mn>1</mn>\u0000 <mo>.</mo>\u0000 <mn>0</mn>\u0000 <mo>]</mo>\u0000 </mrow>\u0000 <annotation>$$ left[0.2,1.0right] $$</annotation>\u0000 </semantics></math>, achieving mean Adjusted Rand Index scores of 0.44. The application to real-world data identifies five distinct clusters through PROCSIMA (PRObabilistic Clustering SIMilarity A","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"41 4","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/asmb.70024","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144339419","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":"An Adaptive Learning Approach to Multivariate Time Forecasting in Industrial Processes","authors":"Fernando Miguelez,&nbsp;Josu Doncel,&nbsp;M. D. Ugarte","doi":"10.1002/asmb.70016","DOIUrl":"https://doi.org/10.1002/asmb.70016","url":null,"abstract":"<p>Industrial processes generate a massive amount of monitoring data that can be exploited to uncover hidden time losses in the system. This can be used to enhance the accuracy of maintenance policies and increase the effectiveness of the equipment. In this work, we propose a method for one-step probabilistic multivariate forecasting of time variables involved in a production process. The method is based on an Input-Output Hidden Markov Model (IO-HMM), in which the parameters of interest are the state transition probabilities and the parameters of the observations' joint density. The ultimate goal of the method is to predict operational process times in the near future, which enables the identification of hidden losses and the location of improvement areas in the process. The input stream in the IO-HMM model includes past values of the response variables and other process features, such as calendar variables, that can have an impact on the model's parameters. The discrete part of the IO-HMM models the operational mode of the process. The state transition probabilities are supposed to change over time and are updated using Bayesian principles. The continuous part of the IO-HMM models the joint density of the response variables. The estimate of the continuous model parameters is recursively computed through an adaptive algorithm that also admits a Bayesian interpretation. The adaptive algorithm allows for efficient updating of the current parameter estimates as soon as new information is available. We evaluate the method's performance using a real data set obtained from a company in a particular sector, and the results are compared with a collection of benchmark models.</p>","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"41 3","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/asmb.70016","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144244145","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":"The Analysis of Association Rules: Sensitivity Analysis","authors":"Ron S. Kenett,&nbsp;Chris Gotwalt","doi":"10.1002/asmb.70022","DOIUrl":"https://doi.org/10.1002/asmb.70022","url":null,"abstract":"<div>\u0000 \u0000 <p>Association rules are extracting information from transactional databases of documents with a collection of items also called “tokens” or “words”. The approach is used in the analysis of text records, of social media and of consumer behaviour. We present an innovative sensitivity analysis of association rules (AR) measures of interest. In text analytics, a document term matrix (DTM) consists of rows referring to documents and columns corresponding to items. In binary weights, “1” indicates the presence of a term in a document and “0” otherwise. From a DTM one computes measures of interest characterising ARs. The approach we introduce is based on the application of befitting cross validation (BCV) principles to ARs. The sensitivity analysis of ARs is based on computer generated repeated shuffling of training and validation sets that provide an assessment of the uncertainty of AR measures of interest. We demonstrate this methodology with reports of symptoms associated with a Nicardipine drug product used in the treatment of high blood pressure and angina. Patients self-reports on side effect events are analysed. Association rules, derived from these reports, describe combinations of terms in these reports. AR measures of interest are defined in section 1. In section 2 we introduce the case study that motivates the method we propose. In section 3 we apply BCV principles by concatenating side effect events of Nicardipine by patient. Sensitivity analysis (SA) of ARs is introduced and demonstrated in section 4. The sensitivity analysis method presented here is discussed in section 5 where we formulate general data analysis considerations on how to organise and analyse semantic data.</p>\u0000 </div>","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"41 3","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144244146","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":"A New Framework to Estimate Return on Investment for Player Salaries in the National Basketball Association","authors":"Jackson P. Lautier","doi":"10.1002/asmb.70020","DOIUrl":"https://doi.org/10.1002/asmb.70020","url":null,"abstract":"<div>\u0000 \u0000 <p>An essential component of financial analysis is a comparison of realized returns. These calculations are straightforward when all cash flows have dollar values. Complexities arise if some flows are nonmonetary, however, such as on-court basketball activities. To our knowledge, this problem remains open. We thus present the first known framework to estimate a return on investment for player salaries in the National Basketball Association (NBA). It is a flexible five-part procedure that includes a novel player credit estimator, the Wealth Redistribution Merit Share (WRMS). The WRMS is a per-game wealth redistribution estimator that allocates fractional performance-based credit to players standardized and centered to uniformity. We show it is asymptotically unbiased to the natural share and simultaneously more robust. The per-game approach allows for break-even analysis between high-performing players with frequent missed games and average-performing players with consistent availability. The WRMS may be used to allocate revenue from a single game to each of its players. Using a player's salary as an initial investment, this creates a sequence of cash flows that may be evaluated using traditional financial analysis. We illustrate all methods with empirical estimates from the 2022–2023 NBA regular season. All data and replication code are made available.</p>\u0000 </div>","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"41 3","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144179402","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":"Comparisons of Coherent Systems' Lifetimes in the Increasing Convex Order","authors":"Francesco Buono,&nbsp;Franco Pellerey","doi":"10.1002/asmb.70021","DOIUrl":"https://doi.org/10.1002/asmb.70021","url":null,"abstract":"<p>Stochastic orders have been widely used in reliability literature to compare the performances of coherent systems, and various criteria have been provided on this purpose. In particular, sufficient conditions have been found for the lifetime of a system to be stochastically larger than that of another system having the same components with identically distributed lifetimes but a different structure function. Known results of this kind concern some of the most relevant stochastic orders, but in the literature no conditions have been provided for the well-known increasing convex order (icx). Here we describe conditions such that two lifetimes of coherent systems are comparable in this stochastic sense when conditions for other stronger orders do not apply. Illustrative examples are also given.</p>","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"41 3","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/asmb.70021","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144179403","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":"Stochastic Modeling and Time-Frequency Analysis for Predictive Maintenance of Automotive Suspension Systems","authors":"Livio Fenga,&nbsp;Luca Biazzo","doi":"10.1002/asmb.70013","DOIUrl":"https://doi.org/10.1002/asmb.70013","url":null,"abstract":"<p>This article presents a real-time predictive maintenance model of vehicle suspensions based on vibration signal analysis. The study is grounded in the observation that suspension wear and failure are primarily driven by cumulative stresses and external shocks encountered during vehicle operation. We use a wavelet-based technique integrated with stochastic modeling and lifetime data analysis to predict the remaining useful life (RUL) of the suspension. The proposed framework provides a decision-making tool for determining whether and when suspension systems should be subjected to inspection, replacement, or overhaul. An empirical application, using vibration data from a uniaxial accelerometer mounted on a vehicle suspension under varying road conditions, validates the theoretical model and estimation procedure.</p>","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"41 3","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/asmb.70013","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144135710","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":"Foreword to the Special Issue on Data Science in Business and Industry","authors":"David Banks,&nbsp;Alba Martínez-Ruiz,&nbsp;David F. Muñoz,&nbsp;Javier Trejos-Zelaya","doi":"10.1002/asmb.70019","DOIUrl":"https://doi.org/10.1002/asmb.70019","url":null,"abstract":"","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"41 3","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144118040","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":"Feature Selection for Stock Movement Direction Prediction Using Sparse Support Vector Machine","authors":"Maoxuan Miao,&nbsp;Jinran Wu,&nbsp;Fengjing Cai,&nbsp;Liya Fu,&nbsp;Shurong Zheng,&nbsp;You-Gan Wang","doi":"10.1002/asmb.70011","DOIUrl":"https://doi.org/10.1002/asmb.70011","url":null,"abstract":"&lt;p&gt;In financial markets, accurate stock price movement prediction can significantly enhance investors' profits. However, the stock price is a highly complex dynamic system with considerable fluctuations, and the accuracy of direction prediction can be improved by selecting appropriate technical indicators. In this work, we propose a novel sparse support vector machines (SVMs) that combines recursive feature elimination (RFE) and ReliefF using a weight parameter. Unlike traditional RFE-based SVMs, our approach constructs a nested feature subset structure, &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;msub&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;F&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mn&gt;1&lt;/mn&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/msub&gt;\u0000 &lt;mo&gt;⊂&lt;/mo&gt;\u0000 &lt;msub&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;F&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mn&gt;2&lt;/mn&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/msub&gt;\u0000 &lt;mo&gt;⊂&lt;/mo&gt;\u0000 &lt;mi&gt;⋯&lt;/mi&gt;\u0000 &lt;mo&gt;⊂&lt;/mo&gt;\u0000 &lt;msub&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;F&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;p&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/msub&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ {F}_1subset {F}_2subset cdots subset {F}_p $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;, using a new filter algorithm that combines backward sacrifice and ReliefF by weighting. This new filter algorithm can capture relevant features and feature interactions simultaneously and is crucial in preventing valuable features from being removed at each iteration. Moreover, the ReliefF algorithm combined with RFE can identify more discriminative feature subsets by reordering the features such that valuable ones are ranked higher than valueless ones, and removing valueless features sequentially through iterative processes. Our experimental results on nine stock datasets from the liquor and spirits concept demonstrate that our proposed method outperforms baseline sparse SVMs and SVM models in terms of accuracy and F-test, while also producing a desirable number of features and automatically eliminating redundancy among technical indicators. We also show that on most stock datasets, the ReliefF algorithm combined with RFE can effectively identify discriminative feature subsets for cases of linear and Gaussian kernel SVMs and our proposed filter method can prevent valuable features from being removed at each iteration. In addition, our experimental findings reveal that feature subsets generated by technical indicators are more discriminative while feature subsets generated by technical i","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"41 3","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/asmb.70011","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144091865","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":"Bayesian Hierarchical Modeling of Noisy Gamma Processes: Formulation and Extensions for Unit-To-Unit Variability","authors":"Ryan Leadbetter,&nbsp;Gabriel González Cáceres,&nbsp;Aloke Phatak","doi":"10.1002/asmb.70014","DOIUrl":"https://doi.org/10.1002/asmb.70014","url":null,"abstract":"<p>The gamma process is a natural model for monotonic degradation processes. In practice, it is desirable to extend the single gamma process to incorporate measurement error and to construct models for the degradation of several nominally identical units. In this paper, we show how these extensions are easily facilitated through the Bayesian hierarchical modeling framework. Following the precepts of the Bayesian statistical workflow, we show the principled construction of a noisy gamma process model. We also reparameterise the gamma process to simplify the specification of priors and make it obvious how the single gamma process model can be extended to include unit-to-unit variability or covariates. We first fit the noisy gamma process model to a single simulated degradation trace. In doing so, we find an identifiability problem between the volatility of the gamma process and the measurement error when there are only a few noisy degradation observations. However, this lack of identifiability can be resolved by including extra information in the analysis through a stronger prior or extra data that informs one of the non-identifiable parameters, or by borrowing information from multiple units. We then explore extensions of the model to account for unit-to-unit variability and demonstrate them using a crack-propagation data set with added measurement error. Lastly, we perform model selection in a fully Bayesian framework by using cross-validation to approximate the expected log probability density of a new observation. We also show how failure time distributions with uncertainty intervals can be calculated for new units or units that are currently under test but have yet to fail.</p>","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"41 3","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/asmb.70014","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144074562","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":"Industrial Statistics in the Knowledge Economy","authors":"David Banks,&nbsp;Yue Li","doi":"10.1002/asmb.70018","DOIUrl":"https://doi.org/10.1002/asmb.70018","url":null,"abstract":"<div>\u0000 \u0000 <p>Industrial statistics grew up in an era when manufacturing was the primary engine of commerce. Today, the driver is information technology. This paper discusses how statisticians need to adapt to contribute to this new business model, with particular emphasis upon computational advertising, autonomous vehicles, operations management, and large language models. Remarkably, many of our old tools are still relevant, even as the new problem space poses fresh research challenges for our employment and educational systems.</p>\u0000 </div>","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"41 3","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144074560","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}