International Statistical Review最新文献

{"title":"The Role of Dice in the Emergence of the Probability Calculus","authors":"David R. Bellhouse,&nbsp;Christian Genest","doi":"10.1111/insr.70003","DOIUrl":"https://doi.org/10.1111/insr.70003","url":null,"abstract":"<p>The early development of the probability calculus was clearly influenced by the roll of dice. However, while dice have been cast since time immemorial, documented calculations on the frequency of various dice throws date back only to the mid-13th century. This paper examines the conceptual changes that took place between the ancient and medieval worlds regarding dice rolls. Archaeological data and written source material are used to explore various notions around tesseræ, or six-sided dice, and tali, which are four-sided dice made from the anklebones of goat or sheep. Given this evidence, it appears that dice rolls provided little or no incentive in Roman society to carry out numerical probability calculations. The first documented frequency computations on dice appeared circa 1260 in the manuscript <i>De vetula</i>, likely written by the English polymath Roger Bacon. In the same period, King Alfonso X of Castile, León and Galicia commissioned a book on games, <i>Libro de los juegos</i>. It is argued that the conceptual changes present in these publications reflect the emerging empiricism, related to dice, and mathematical interests of Bacon and his Islamic predecessors.</p>","PeriodicalId":14479,"journal":{"name":"International Statistical Review","volume":"94 2","pages":"464-483"},"PeriodicalIF":1.9,"publicationDate":"2026-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/insr.70003","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148696650","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine Learning: A Concise Introduction, 2nd Edition, Steven W. Knox Wiley, 2026, xiv + 419 pages, $109.95, hardcover ISBN: 978-1-394-32525-2","authors":"Alastair Young","doi":"10.1111/insr.70055","DOIUrl":"https://doi.org/10.1111/insr.70055","url":null,"abstract":"","PeriodicalId":14479,"journal":{"name":"International Statistical Review","volume":"94 2","pages":"494-495"},"PeriodicalIF":1.9,"publicationDate":"2026-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148696224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A New Evaluation Strategy for Diagnostic Tests Under Umbrella or Tree Ordering","authors":"Duc-Khanh To,&nbsp;Gianfranco Adimari,&nbsp;Monica Chiogna","doi":"10.1111/insr.12612","DOIUrl":"https://doi.org/10.1111/insr.12612","url":null,"abstract":"<div>\u0000 \u0000 <p>In medical research, diagnostic tests (or biomarkers) with continuous values are widely employed to attempt to distinguish between diseased and non-diseased subjects. The diagnostic accuracy of a test can be assessed by its receiver operating characteristic (ROC) curve. For diseases with multiclasses, an important category of scenarios assumes tree or umbrella ordering, where the test measurement for one particular class is lower or higher than those for the other classes. In this paper, we propose a new ROC framework for tree or umbrella ordering, together with a related evaluation strategy. Such a strategy is based on new ROC representations on the plane, denoted as LTROC and UTROC, and new summary indexes. Related statistical inference is also discussed. In particular, we propose simple estimation and interval estimation procedures, in a nonparametric setting. For these procedures, we provide theoretical justification and assess the behaviour in finite samples through simulation experiments. Finally, we illustrate the proposed approach with two real data examples.</p>\u0000 </div>","PeriodicalId":14479,"journal":{"name":"International Statistical Review","volume":"94 2","pages":"242-263"},"PeriodicalIF":1.9,"publicationDate":"2026-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148696296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Uncertainty Based Approach for Dealing With Selection Bias in Non-Probability Samples","authors":"Pier Luigi Conti,&nbsp;Daniela Marella","doi":"10.1111/insr.12614","DOIUrl":"https://doi.org/10.1111/insr.12614","url":null,"abstract":"<p>The main issue with non-probability samples is that the standard design-based approach cannot be applied as the selection mechanism is unknown. In this paper, the concept of uncertainty on data generating model, resulting from the lack of knowledge of the sampling design acting in the non-probability sample, is discussed. Furthermore, the effect on uncertainty due to the availability of extra-sample information is evaluated. First of all, the class of plausible distributions for the variable of interest is defined, a measure of uncertainty is introduced and its asymptotic properties are analysed. Next, a plausible estimate of the distribution of the variable of interest is constructed and its accuracy is evaluated. Finally, a simulation study is performed, and an application to a real case is provided.</p>","PeriodicalId":14479,"journal":{"name":"International Statistical Review","volume":"94 2","pages":"264-290"},"PeriodicalIF":1.9,"publicationDate":"2026-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/insr.12614","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148696362","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fundamentals of Robust Machine Learning: Handling Outliers and Anomalies in Data Science, Resve Saleh, Sohaib Majzoub, A.K. Md. Ehsanes Saleh, John Wiley & Sons, 2025, xiv + 416 pages, £103.95, hardcover. ISBN: 978-1-394-29437-4","authors":"Zhilin Xiong,&nbsp;Tiefeng Ma","doi":"10.1111/insr.70048","DOIUrl":"https://doi.org/10.1111/insr.70048","url":null,"abstract":"","PeriodicalId":14479,"journal":{"name":"International Statistical Review","volume":"94 2","pages":"489-491"},"PeriodicalIF":1.9,"publicationDate":"2026-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148696415","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Medical Knowledge Integration Into Reinforcement Learning Algorithms for Dynamic Treatment Regimes","authors":"Sophia Yazzourh,&nbsp;Nicolas Savy,&nbsp;Philippe Saint-Pierre,&nbsp;Michael R. Kosorok","doi":"10.1111/insr.12617","DOIUrl":"https://doi.org/10.1111/insr.12617","url":null,"abstract":"<p>The goal of precision medicine is to provide individualised treatment at each stage of chronic diseases, a concept formalised by dynamic treatment regimes (DTR). These regimes adapt treatment strategies based on decision rules learned from clinical data to enhance therapeutic effectiveness. Reinforcement learning (RL) algorithms allow to determine these decision rules conditioned by individual patient data and their medical history. The integration of medical expertise into these models makes possible to increase confidence in treatment recommendations and facilitate the adoption of this approach by healthcare professionals and patients. In this work, we examine the mathematical foundations of RL, contextualise its application in the field of DTR, and present an overview of methods to improve its effectiveness by integrating medical expertise.</p>","PeriodicalId":14479,"journal":{"name":"International Statistical Review","volume":"94 2","pages":"382-411"},"PeriodicalIF":1.9,"publicationDate":"2026-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/insr.12617","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148695810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine Learning in Finance: Trends, Developments and Business Practices in the Financial Sector Edited by Musa Gun and Burcu Kartal Springer Nature Switzerland AG, 2025, xi + 204 pages, £149.99, hardcover ISBN: 978-3-031-83265-9 (print); 978-3-031-83266-6 (eBook)","authors":"Svetlozar Rachev,&nbsp;Zari","doi":"10.1111/insr.70045","DOIUrl":"https://doi.org/10.1111/insr.70045","url":null,"abstract":"","PeriodicalId":14479,"journal":{"name":"International Statistical Review","volume":"94 2","pages":"487-489"},"PeriodicalIF":1.9,"publicationDate":"2026-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148695886","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pre-Smoothing Methods for Transition Probabilities in Complex Non-Markovian Multistate Models","authors":"Gustavo Soutinho,&nbsp;Luís Meira-Machado","doi":"10.1111/insr.70002","DOIUrl":"https://doi.org/10.1111/insr.70002","url":null,"abstract":"<div>\u0000 \u0000 <p>Multistate models are essential tools in longitudinal data analysis, enabling the estimation of transition probabilities that provide predictive insights into clinical outcomes across stages of disease progression or recovery. Conventional approaches to inference in these models often rely on the Markov assumption, which simplifies computation but may not hold in complex real-world settings. To address this limitation, we extend the landmark Aalen–Johansen estimator by incorporating pre-smoothing techniques, offering a robust alternative for estimating transition probabilities in non-Markovian multistate models, including those with multiple states and reversible transitions. The proposed method effectively reduces estimation variability and mitigates biases arising from the selection of arbitrary landmark times. Through empirical evaluation using three real-world datasets with distinct multistate structures, we demonstrate that the pre-smoothed estimator achieves enhanced precision and stability, particularly in the presence of high noise or small sample sizes. To facilitate its application, we provide an R package, <span>presmoothedTP</span>, which implements all the proposed methods.</p>\u0000 </div>","PeriodicalId":14479,"journal":{"name":"International Statistical Review","volume":"94 2","pages":"445-463"},"PeriodicalIF":1.9,"publicationDate":"2026-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148696429","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Recent Developments on Statistical Transfer Learning","authors":"Zhengyu Zhu,&nbsp;Yibo Yan,&nbsp;Gefei Li,&nbsp;Riquan Zhang","doi":"10.1111/insr.12613","DOIUrl":"https://doi.org/10.1111/insr.12613","url":null,"abstract":"<div>\u0000 \u0000 <p>In recent years, there has been a significant increase in scholarly research on transfer learning. This review provides an in-depth examination of statistical transfer learning, categorising challenges into two domains: model-based and data distribution-based transfer learning. For model-based transfer learning, we assume sufficient similarity between the source and target domain models, which allows the effective use of source data for target tasks. This framework applies to various problems, including (generalised) linear regression, quantile regression, kernel regression, functional regression and graphical model. From another point of view, data distribution-based transfer learning addresses variations due to covariate shift and posterior drift, enhancing the reliability of shared data from diverse sources. The primary objective is to introduce typical methods and data structures adapted for transfer learning, along with resolution techniques. Additionally, we elucidate theoretical concepts such as consistency and minimax optimality to provide a comprehensive understanding of the subject. Furthermore, we explore other related topics in statistical transfer learning that tackle various problems.</p>\u0000 </div>","PeriodicalId":14479,"journal":{"name":"International Statistical Review","volume":"94 2","pages":"291-324"},"PeriodicalIF":1.9,"publicationDate":"2026-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148696380","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparing Robust Versions of Distance Covariance: A Comment on the Biloop Approach","authors":"Dominic Edelmann","doi":"10.1111/insr.70012","DOIUrl":"https://doi.org/10.1111/insr.70012","url":null,"abstract":"&lt;p&gt;I commend the authors for their significant contributions to the field of distance correlation presented in this paper. This work marks the first thorough investigation of the robustness properties of distance correlation, distance covariance, and distance standard deviation in terms of influence functions and breakdown points. These robustness aspects have previously caused considerable confusion in the literature, and this work will serve as an important and clarifying reference. Additionally, the authors introduce a novel version of distance covariance based on an innovative biloop transformation. Thanks to its redescending influence function, this version of distance covariance is robust to bivariate outliers and shows promise for real-world applications.&lt;/p&gt;&lt;p&gt;However, the paper does not fully capture the extensive literature on robust distance covariance measures. Criticism regarding the lack of robustness in classical distance covariance can be traced back to the 2009 discussion paper of Székely and Rizzo, in which Bruno Rémillard (&lt;span&gt;2009&lt;/span&gt;) identifies the moment assumption as a weakness of distance covariance and suggests a rank transformation as a remedy. In the same discussion, Gretton &lt;i&gt;et al.&lt;/i&gt; (&lt;span&gt;2009&lt;/span&gt;) give a brief historic overview and present several robust distance covariance type statistics (Kankainen, &lt;span&gt;1995&lt;/span&gt;; Feuerverger, &lt;span&gt;1993&lt;/span&gt;).&lt;/p&gt;&lt;p&gt;In this comment, I offer a concise, albeit non-comprehensive, overview of robust versions of distance covariance. Moreover, I extend the simulation studies presented in the main paper, giving further insights into the properties of the newly proposed biloop distance covariance.&lt;/p&gt;&lt;p&gt;The literature offers at least three different approaches to defining extensions of distance covariance. Each approach gives rise to dependence measures with positive breakdown points.&lt;/p&gt;&lt;p&gt;The simulations presented below extend those in the main paper to shed further light on the properties of biloop distance covariance. Since the results of the normal score distance covariance were very similar to the rank distance covariance, the former method has been omitted. Instead the distance covariance \u0000&lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mspace&gt;&lt;/mspace&gt;\u0000 &lt;msub&gt;\u0000 &lt;mtext&gt;dCov&lt;/mtext&gt;\u0000 &lt;mi&gt;d&lt;/mi&gt;\u0000 &lt;/msub&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ kern0.1em {mathrm{dCov}}_d $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; in Equation (2) employing the RBF distance (Equation 2) is included; this method is denoted as &lt;i&gt;RBF distance covariance&lt;/i&gt; in the following. The hyperparameter \u0000&lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;c&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ c $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; for the biloop distance","PeriodicalId":14479,"journal":{"name":"International Statistical Review","volume":"94 1","pages":"26-34"},"PeriodicalIF":1.8,"publicationDate":"2026-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/insr.70012","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147683056","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}