International Statistical Review最新文献

{"title":"A First Course in Statistical Learning, With Data Examples and Python Code Johannes Lederer Springer, 2025, xiv + 282 pages, £79.99, hardcover ISBN: 978-3-031-30276-3","authors":"Rauf Ahmad","doi":"10.1111/insr.70054","DOIUrl":"https://doi.org/10.1111/insr.70054","url":null,"abstract":"","PeriodicalId":14479,"journal":{"name":"International Statistical Review","volume":"94 2","pages":"492-493"},"PeriodicalIF":1.9,"publicationDate":"2026-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148695864","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":"Data Science and Risk Analytics in Finance and Insurance Tze Leung Lai and Haipeng Xing, Chapman & Hall/CRC Financial Mathematics Series, 2024, 366 pages, $99.99, hardcover; $94.99, Kindle. ISBN: 978-1-4398-3948-5","authors":"Svetlozar Rachev, Zari","doi":"10.1111/insr.70044","DOIUrl":"https://doi.org/10.1111/insr.70044","url":null,"abstract":"","PeriodicalId":14479,"journal":{"name":"International Statistical Review","volume":"94 2","pages":"486-487"},"PeriodicalIF":1.9,"publicationDate":"2026-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148695885","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":"High-dimensional Probability: An Introduction with applications in data science, 2nd Edition Roman VershyninCambridge University Press, 2026, viii + 333 pages, £65, hardcover ISBN: 9781009490672","authors":"Rauf Ahmad","doi":"10.1111/insr.70057","DOIUrl":"https://doi.org/10.1111/insr.70057","url":null,"abstract":"","PeriodicalId":14479,"journal":{"name":"International Statistical Review","volume":"94 2","pages":"495-496"},"PeriodicalIF":1.9,"publicationDate":"2026-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148695896","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":"Applied Time Series Analysis for the Social Sciences: Specification, Estimation and Inference Regina M. Baker John Wiley & Sons, 2026, xiii + 226 pages, $76.95, hardcover ISBN: 978-0-470-74993-7","authors":"Johan Lyhagen","doi":"10.1111/insr.70058","DOIUrl":"https://doi.org/10.1111/insr.70058","url":null,"abstract":"","PeriodicalId":14479,"journal":{"name":"International Statistical Review","volume":"94 2","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148696072","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":"New Perspectives in Mathematical and Statistical Methods for Actuarial Sciences and Finance, Michele La Rocca, Massimiliano Menzietti, Cira Perna, and Marilena Sibillo, Springer Nature Switzerland AG, 2025, viii + 261 pages, £119.99, hardcover. ISBN: 978-3-032-05550-7 (print); 978-3-032-05551-4 (eBook)","authors":"Svetlozar Rachev, Zari","doi":"10.1111/insr.70046","DOIUrl":"https://doi.org/10.1111/insr.70046","url":null,"abstract":"","PeriodicalId":14479,"journal":{"name":"International Statistical Review","volume":"94 2","pages":"484-485"},"PeriodicalIF":1.9,"publicationDate":"2026-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148696439","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":"Master Protocol Clinical Trials for Evidence Generation: Strategies, Designs, Operations and Case Studies, Ruixiao Lu, Jingjing Ye, Chengxing (Cindy) Lu, William Wang, Chapman & Hall. CRC Biostatistics Series, 2026, 436 pages, £140.00, hardcover ISBN: 978-1-032-54452-6","authors":"Debashis Ghosh","doi":"10.1111/insr.70051","DOIUrl":"https://doi.org/10.1111/insr.70051","url":null,"abstract":"","PeriodicalId":14479,"journal":{"name":"International Statistical Review","volume":"94 2","pages":"491-492"},"PeriodicalIF":1.9,"publicationDate":"2026-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148696471","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":"Inference via Robust Optimal Transportation: Theory and Methods","authors":"Yiming Ma,&nbsp;Hang Liu,&nbsp;Davide La Vecchia,&nbsp;Matthieu Lerasle","doi":"10.1111/insr.70000","DOIUrl":"https://doi.org/10.1111/insr.70000","url":null,"abstract":"<div>\u0000 \u0000 <p>Optimal transportation (OT) is widely applied in statistics and machine learning. Despite its popularity, inference based on OT has some issues. For instance, it is sensitive to outliers and may not be even defined when the underlying model has infinite moments. To cope with these problems, first, we consider a robust version of the primal transportation problem and show that it defines the robust Wasserstein distance, \u0000<span></span><math>\u0000 <msup>\u0000 <mrow>\u0000 <mi>W</mi>\u0000 </mrow>\u0000 <mrow>\u0000 <mo>(</mo>\u0000 <mi>λ</mi>\u0000 <mo>)</mo>\u0000 </mrow>\u0000 </msup></math>, depending on a tuning parameter \u0000<span></span><math>\u0000 <mi>λ</mi>\u0000 <mo>&gt;</mo>\u0000 <mn>0</mn></math>. Second, we illustrate the link between 1-Wasserstein distance \u0000<span></span><math>\u0000 <msub>\u0000 <mrow>\u0000 <mi>W</mi>\u0000 </mrow>\u0000 <mrow>\u0000 <mn>1</mn>\u0000 </mrow>\u0000 </msub></math> and \u0000<span></span><math>\u0000 <msup>\u0000 <mrow>\u0000 <mi>W</mi>\u0000 </mrow>\u0000 <mrow>\u0000 <mo>(</mo>\u0000 <mi>λ</mi>\u0000 <mo>)</mo>\u0000 </mrow>\u0000 </msup></math> and study its key measure theoretic aspects. Third, we derive some concentration inequalities for \u0000<span></span><math>\u0000 <msup>\u0000 <mrow>\u0000 <mi>W</mi>\u0000 </mrow>\u0000 <mrow>\u0000 <mo>(</mo>\u0000 <mi>λ</mi>\u0000 <mo>)</mo>\u0000 </mrow>\u0000 </msup></math>. Fourth, we use \u0000<span></span><math>\u0000 <msup>\u0000 <mrow>\u0000 <mi>W</mi>\u0000 </mrow>\u0000 <mrow>\u0000 <mo>(</mo>\u0000 <mi>λ</mi>\u0000 <mo>)</mo>\u0000 </mrow>\u0000 </msup></math> to define minimum distance estimators, provide their statistical guarantees and illustrate how to apply the concentration inequalities for a selection of \u0000<span></span><math>\u0000 <mi>λ</mi></math>. Fifth, we provide the dual form of the robust optimal transportation (ROBOT) and apply it to machine learning problems. We review, in a unified perspective, the key aspects of OT and ROBOT, while complementing the existing results with our new methodological findings. Numerical exercises provide evidence of the benefits of our novel methods.</p>\u0000 </div>","PeriodicalId":14479,"journal":{"name":"International Statistical Review","volume":"94 2","pages":"412-444"},"PeriodicalIF":1.9,"publicationDate":"2026-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148696470","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 Comparative Review of Specification Tests for Diffusion Models","authors":"A. López-Pérez,&nbsp;D. Diz-Castro,&nbsp;M. Febrero-Bande,&nbsp;W. González-Manteiga","doi":"10.1111/insr.12616","DOIUrl":"https://doi.org/10.1111/insr.12616","url":null,"abstract":"<p>Diffusion models play an essential role in modelling continuous-time stochastic processes in the financial field. Therefore, several proposals have been developed in the last decades to test the specification of stochastic differential equations. We provide a survey to collect some developments on goodness-of-fit tests for diffusion models and implement some of these methods to illustrate their finite sample behaviour, regarding size and power, by means of a simulation study. We also apply the ideas of distance covariance to propose a test for the parametric specification of diffusion models, comparing its performance with the other methods and analyzing the effect of the curse of dimensionality. As real data examples, treasury securities with different maturities are considered.</p>","PeriodicalId":14479,"journal":{"name":"International Statistical Review","volume":"94 2","pages":"347-381"},"PeriodicalIF":1.9,"publicationDate":"2026-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/insr.12616","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148696627","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":"On Metric Choice in Dimension Reduction for Fréchet Regression","authors":"Abdul-Nasah Soale,&nbsp;Congli Ma,&nbsp;Siyu Chen,&nbsp;Obed Koomson","doi":"10.1111/insr.12615","DOIUrl":"https://doi.org/10.1111/insr.12615","url":null,"abstract":"<p>Fréchet regression is becoming a mainstay in modern data analysis for analysing non-traditional data types belonging to general metric spaces. This novel regression method is especially useful in the analysis of complex health data such as continuous monitoring and imaging data. Fréchet regression utilises the pairwise distances between the random objects, which makes the choice of metric crucial in the estimation. In this paper, existing dimension reduction methods for Fréchet regression are reviewed, and the effect of metric choice on the estimation of the dimension reduction subspace is explored for the regression between random responses and Euclidean predictors. An extensive numerical study illustrate how different metrics affect the central and central mean space estimators. Two real applications involving analysis of brain connectivity networks of subjects with and without Parkinson's disease and an analysis of the distributions of glycaemia based on continuous glucose monitoring data are provided, to demonstrate how metric choice can influence findings in real applications.</p>","PeriodicalId":14479,"journal":{"name":"International Statistical Review","volume":"94 2","pages":"325-346"},"PeriodicalIF":1.9,"publicationDate":"2026-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/insr.12615","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148696271","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":"A Conversation With David Bellhouse","authors":"Christian Genest","doi":"10.1111/insr.12604","DOIUrl":"https://doi.org/10.1111/insr.12604","url":null,"abstract":"<p>David Richard Bellhouse was born in Winnipeg, Manitoba, on 19 July 1948. He studied actuarial mathematics and statistics at the University of Manitoba (BA, 1970; MA, 1972) and completed his PhD at the University of Waterloo, Ontario, in 1975. After being an Assistant Professor for 1 year at his alma mater, he joined the University of Western Ontario in 1975, where he was a Staff Member in the Social Science Computing Laboratory before becoming an Assistant Professor of Statistics in 1977. He was tenured in 1982, promoted to the rank of Full Professor in 1987, chaired his department from 1992 to 1999 and retired in 2016. He is the author or coauthor of eight books and over 100 research papers in survey sampling and in the history of probability and statistics. In addition to his methodological contributions, he did much collaborative work in fields such as archaeology, law and medicine. His acclaimed biographies of Thomas Bayes and Abraham De Moivre also brought to light many new facts about these pioneers. During his career, David supervised 45 MSc students, 4 PhD students and 2 postdoctoral fellows. He also served the community in various ways, notably as an Associate Editor for five different journals, including the <i>Journal of the American Statistical Association</i> (1983–1988) and <i>Survey Methodology</i> (1985–2006). Moreover, he was Secretary (1988–1990) and later President (1998–1999) of the Statistical Society of Canada (SSC). He is an Elected Member of the International Statistical Institute (1987), a Fellow of the American Statistical Association (1989) and an Honorary Member of the SSC (2017). Moreover, he received the University of Western Ontario Gold Medal for Excellence in Teaching (1985), the SSC Service Award (2003) and the 2017 University of Manitoba Faculty of Science Alumni Award. Since 2020, he is also an Honorary Fellow of the Institute and Faculty of Actuaries of the United Kingdom.</p>","PeriodicalId":14479,"journal":{"name":"International Statistical Review","volume":"94 2","pages":"221-241"},"PeriodicalIF":1.9,"publicationDate":"2026-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/insr.12604","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148696452","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}