Applied Stochastic Models in Business and Industry最新文献

{"title":"Rejoinder to Next Generation Models for Subsequent Sports Injuries by Wu et al.","authors":"Paul Pao-Yen Wu, Yu Yi Yu, Liam A. Toohey, Michael Drew, Scott A. Sisson, Clara Grazian, Kerrie Mengersen","doi":"10.1002/asmb.70035","DOIUrl":"https://doi.org/10.1002/asmb.70035","url":null,"abstract":"<p>We greatly appreciate the commentary and positive feedback of discussants Prof. Jialiang Li and Dr. Rhythm Grover to enrich our paper and its context.</p><p>As noted by Prof. Li, survival models are highly applicable to the subsequent sports injury problem given the temporal dimension of injury data. In the sporting context, censoring can arise, for example, from finite surveillance windows associated with a sporting season, athletes joining and leaving a team, or even extended absence due to injury [<span>1, 2</span>]. However, given the complex systems nature of individual athletes and potentially changing dynamics and susceptibility to injury over time, it is also important to capture the changing state of the athlete explicitly [<span>3</span>]. For example, increasing strength with training over a season could reduce injury risk; however, a serious injury such as an ACL injury could lead to increased susceptibility to subsequent injuries.</p><p>Our paper presented a pragmatic approach, as noted by Dr. Grover, to tackle the challenges of modeling subsequent injury, reducing dimensionality through a time-varying Cox Proportional Hazards (PH) model, and using a discrete-time HMM to capture changes in susceptibility and covariate effects over time. Both Prof. Li and Dr. Grover note the potential computational challenge associated with Hidden Markov Models (HMMs) especially in the presence of large-scale and high-dimensional datasets. Hence, the need for dimension reduction, which was undertaken using survival modeling to explicitly cater for the time-to-event nature of injury data and censoring. The appropriateness of using the survival model was supported by checks of the assumptions of the PH model (e.g., proportional hazards, Schoenfeld residuals) and validation results (concordance index) as reported in our paper.</p><p>In addition to computational complexity, however, is the somewhat associated challenge of model convergence. Greater model complexity, such as more HMM states or more model covariates, can lead to challenges with model identifiability, estimation, computation, and thus model convergence [<span>4</span>]. This is a current research challenge when faced with limited data as in our subsequent injury application, which is limited to 33 players and 2523 training and competition sessions over one season. Computationally, the proposed discrete-time HMM fitted with Expectation Maximization (EM) took approximately 155 s to converge for the entire team of players over one season, compared to less than a second for the Cox PH model. However, model convergence with more than two states could not be achieved with this limited dataset. Therefore, although the computational cost is feasible in this case study, the data available can limit the level of model complexity that can be achieved. Hence, it highlights the utility of the proposed combination of dimension reduction and state space modelling as a more generalizable approach, and th","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"41 4","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/asmb.70035","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144725648","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":"Joint Tail Probability of Renewal Models of Dependent Heavy-Tailed Random Variables With Applications to Systemic Risk Measures","authors":"Lei Zou,&nbsp;Jiangyan Peng,&nbsp;Chenghao Xu","doi":"10.1002/asmb.70028","DOIUrl":"https://doi.org/10.1002/asmb.70028","url":null,"abstract":"<div>\u0000 \u0000 <p>Consider a non-standard renewal risk model in which claims arrive in pairs <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mo>{</mo>\u0000 <mo>(</mo>\u0000 <msub>\u0000 <mrow>\u0000 <mi>X</mi>\u0000 </mrow>\u0000 <mrow>\u0000 <mn>1</mn>\u0000 <mi>i</mi>\u0000 </mrow>\u0000 </msub>\u0000 <mo>,</mo>\u0000 <msub>\u0000 <mrow>\u0000 <mi>X</mi>\u0000 </mrow>\u0000 <mrow>\u0000 <mn>2</mn>\u0000 <mi>i</mi>\u0000 </mrow>\u0000 </msub>\u0000 <mo>)</mo>\u0000 <mo>;</mo>\u0000 <mi>i</mi>\u0000 <mo>∈</mo>\u0000 <mi>ℕ</mi>\u0000 <mo>}</mo>\u0000 </mrow>\u0000 <annotation>$$ left{left({X}_{1i},{X}_{2i}right);iin mathbb{N}right} $$</annotation>\u0000 </semantics></math> and the stochastic discounting process is given by <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mo>{</mo>\u0000 <msup>\u0000 <mrow>\u0000 <mi>e</mi>\u0000 </mrow>\u0000 <mrow>\u0000 <mo>−</mo>\u0000 <mi>ξ</mi>\u0000 <mo>(</mo>\u0000 <mi>t</mi>\u0000 <mo>)</mo>\u0000 </mrow>\u0000 </msup>\u0000 <mo>;</mo>\u0000 <mi>t</mi>\u0000 <mo>≥</mo>\u0000 <mn>0</mn>\u0000 <mo>}</mo>\u0000 </mrow>\u0000 <annotation>$$ left{{e}^{-xi (t)};tge 0right} $$</annotation>\u0000 </semantics></math>, where <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>ξ</mi>\u0000 <mo>(</mo>\u0000 <mo>·</mo>\u0000 <mo>)</mo>\u0000 </mrow>\u0000 <annotation>$$ xi left(cdotp right) $$</annotation>\u0000 </semantics></math> is a Lévy process. We are interested in the joint tail probability of <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msub>\u0000 <mrow>\u0000 <mi>L</mi>\u0000 </mrow>\u0000 <mrow>\u0000 <mn>1</mn>\u0000 </mrow>\u0000 </msub>\u0000 <mo>(</mo>\u0000 <mi>t</mi>\u0000 <mo>)</mo>\u0000 </mrow>\u0000 <annotation>$$ {L}_1(t) $$</annotation>\u0000 </semantics></math> and <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msub>\u0000 <mrow>\u0000 <mi>L</mi>\u0000 </mrow>\u0000 ","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"41 4","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144725612","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":"Enhancing Credit Risk Management Through Integration of Multiple Imputation Methodology and Long-Term Survival Modelling","authors":"Jacob Majakwara,&nbsp;Patrick L. Mthisi,&nbsp;Honest W. Chipoyera","doi":"10.1002/asmb.70027","DOIUrl":"https://doi.org/10.1002/asmb.70027","url":null,"abstract":"<p>Credit risk management plays a crucial role in financial institutions by identifying, assessing and controlling the credit risks arising from lending activities. However, missing data pose a common problem in credit risk modelling, leading to biased estimates and a loss of statistical power. To address this issue and improve predictive accuracy, multiple imputation methods are increasingly employed. This study evaluates the performance of the Multivariate Imputation by Chained Equations (MICE) method in identifying factors associated with time to default, using the publicly available Prosper personal loan data. The analysis is conducted within the framework of mixture cure rate models based on the generalised gamma family of distributions. This research is the first of its kind to integrate the MICE approach into mixture cure rate modelling. The flexibility of the generalised gamma distribution was utilised to select the optimal mixture cure rate model. The estimated cure rate using complete cases (CC) was higher than that obtained using MICE imputation. This highlights the potential pitfalls of solely relying on CC analysis in survival analysis.</p>","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"41 4","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/asmb.70027","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144673029","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":"Forecasting Inflation From Disaggregated Data","authors":"Wilmer Martínez-Rivera,&nbsp;Eliana González-Molano,&nbsp;Edgar Caicedo-Garcia","doi":"10.1002/asmb.70023","DOIUrl":"https://doi.org/10.1002/asmb.70023","url":null,"abstract":"<div>\u0000 \u0000 <p>We forecast inflation aggregates for the United States, the United Kingdom, and Colombia using forecasts aggregation of disaggregates and forecasts obtained directly from the aggregate. We implement helpful models for many predictors, such as dimension reduction, shrinkage methods, machine learning models, and traditional time-series models (ARIMA and TAR). We evaluate out-sample forecasts for the period before COVID-19 and the period afterward. It was found that the aggregation of forecasts performs as well as the forecast using the aggregate directly. In some cases, there is a reduction in the forecast error from the disaggregate analysis.</p>\u0000 </div>","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"41 4","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144573576","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":"Data Quality: What if Deming Were Born Today?","authors":"Dennis K. J. Lin,&nbsp;Nicholas Rios","doi":"10.1002/asmb.70025","DOIUrl":"https://doi.org/10.1002/asmb.70025","url":null,"abstract":"<p>If Francis Bacon were born today, he might have said “data is power” instead of his original saying, “knowledge is power.” In modern society, data is everywhere. In memory of Deming (a guru in quality), this paper attempts to address the fundamental issue of data quality and how Deming would handle it. Specifically, we attempt to explain what data quality really means, and the critical impact that it has on data science. Statisticians, who understand how to collect high quality data, have much more to contribute to both the intellectual vitality and the practical utility of data science. At the same time, data science challenges statisticians to move out of some familiar habits to engage less structured problems, to become more comfortable with ambiguity, and to engage more scientists in a fruitful discussion on what various parties can bring to this new mode of investigation. Some potential avenues for future research in the collection of high-quality data will be proposed.</p>","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"41 4","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/asmb.70025","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144514730","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":"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":"&lt;p&gt;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 &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mo&gt;(&lt;/mo&gt;\u0000 &lt;mi&gt;n&lt;/mi&gt;\u0000 &lt;mo&gt;=&lt;/mo&gt;\u0000 &lt;mn&gt;114&lt;/mn&gt;\u0000 &lt;mo&gt;,&lt;/mo&gt;\u0000 &lt;mn&gt;000&lt;/mn&gt;\u0000 &lt;mo&gt;)&lt;/mo&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ left(n=114,000right) $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; 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 &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mo&gt;[&lt;/mo&gt;\u0000 &lt;mn&gt;0&lt;/mn&gt;\u0000 &lt;mo&gt;.&lt;/mo&gt;\u0000 &lt;mn&gt;4&lt;/mn&gt;\u0000 &lt;mo&gt;,&lt;/mo&gt;\u0000 &lt;mn&gt;1&lt;/mn&gt;\u0000 &lt;mo&gt;.&lt;/mo&gt;\u0000 &lt;mn&gt;0&lt;/mn&gt;\u0000 &lt;mo&gt;]&lt;/mo&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ left[0.4,1.0right] $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; and emotion strength in &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mo&gt;[&lt;/mo&gt;\u0000 &lt;mn&gt;0&lt;/mn&gt;\u0000 &lt;mo&gt;.&lt;/mo&gt;\u0000 &lt;mn&gt;2&lt;/mn&gt;\u0000 &lt;mo&gt;,&lt;/mo&gt;\u0000 &lt;mn&gt;1&lt;/mn&gt;\u0000 &lt;mo&gt;.&lt;/mo&gt;\u0000 &lt;mn&gt;0&lt;/mn&gt;\u0000 &lt;mo&gt;]&lt;/mo&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ left[0.2,1.0right] $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;, 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}