International Statistical Review最新文献

{"title":"New Scheme of Empirical Likelihood Method for Ranked Set Sampling: Applications to Two One‐Sample Problems","authors":"Soohyun Ahn, Xinlei Wang, Chul Moon, Johan Lim","doi":"10.1111/insr.12589","DOIUrl":"https://doi.org/10.1111/insr.12589","url":null,"abstract":"We propose a novel empirical likelihood (EL) approach for ranked set sampling (RSS) that leverages the ranking structure and information of the RSS. Our new proposal suggests constraining the sum of the within‐stratum probabilities of each rank stratum to , where is the number of rank strata. The use of the additional constraints eliminates the need of subjective weight selection in unbalanced RSS and facilitates a seamless extension of the method for balanced RSS to unbalanced RSS. We apply our new proposal to testing one sample population mean and evaluate its performance through a numerical study and two real‐world data sets, examining obesity from body fat data and symmetry of dental size from human tooth size data. We further consider the extension of the proposed EL method to jackknife EL.","PeriodicalId":14479,"journal":{"name":"International Statistical Review","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141929063","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":"Validating an Index of Selection Bias for Proportions in Non‐Probability Samples","authors":"Angelina Hammon, Sabine Zinn","doi":"10.1111/insr.12590","DOIUrl":"https://doi.org/10.1111/insr.12590","url":null,"abstract":"SummaryFast online surveys without sampling frames are becoming increasingly important in survey research. Their recruitment methods result in non‐probability samples. As the mechanism of data generation is always unknown in such samples, the problem of non‐ignorability arises making vgeneralisation of calculated statistics to the population of interest highly questionable. Sensitivity analyses provide a valuable tool to deal with non‐ignorability. They capture the impact of different sample selection mechanisms on target statistics. In 2019, Andridge and colleagues proposed an index to quantify potential (non‐ignorable) selection bias in proportions that combines the effects of different selection mechanisms. In this paper, we validate this index with an artificial non‐probability sample generated from a large empirical data set and additionally applied it to proportions estimated from data on current political attitudes arising from a real non‐probability sample selected via River sampling. We find a number of conditions that must be met for the index to perform meaningfully. When these requirements are fulfilled, the index shows an overall good performance in both of our applications in detecting and correcting present selection bias in estimated proportions. Thus, it provides a powerful measure for evaluating the robustness of results obtained from non‐probability samples.","PeriodicalId":14479,"journal":{"name":"International Statistical Review","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141933218","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 Interview With Peter Rousseeuw","authors":"Mia Hubert","doi":"10.1111/insr.12587","DOIUrl":"https://doi.org/10.1111/insr.12587","url":null,"abstract":"SummaryPeter J. Rousseeuw is a statistician known mainly for his work on robust statistics and cluster analysis. Among his creations are least trimmed squares regression, the minimum covariance determinant estimator, the partitioning around medoids clustering method and the silhouettes graphical display. Peter obtained his PhD in 1981 following research carried out at the ETH in Zürich, Switzerland, which led to a book on influence functions. Later, he was a professor at Delft University of Technology, The Netherlands, and at the University of Antwerp, Belgium. Next, he was a researcher at Renaissance Technologies in New York for over a decade. He then returned to Belgium as a full professor at KU Leuven, until becoming emeritus in 2022. He is an elected member of the International Statistical Institute and a fellow of the Institute of Mathematical Statistics and the American Statistical Association. In the course of his career, Peter published three books and over 200 papers, together receiving over 100 000 citations. He was awarded the George Box Medal for Business and Industrial Statistics, the Research Medal of the International Federation of Classification Societies, the Frank Wilcoxon Prize, and twice the Jack Youden Prize. Recently, Peter received the 2024 ASA Noether Distinguished Scholar Award for nonparametric statistics. His former PhD students include Annick Leroy, Rik Lopuhaä, Geert Molenberghs, Christophe Croux, Mia Hubert, Stefan Van Aelst, Tim Verdonck and Jakob Raymaekers. He is the creator and sole sponsor of the Rousseeuw Prize for Statistics, which was first handed out by the King of Belgium in 2022.","PeriodicalId":14479,"journal":{"name":"International Statistical Review","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141933217","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":"Reinforcement Learning in Modern Biostatistics: Constructing Optimal Adaptive Interventions","authors":"Nina Deliu, Joseph Jay Williams, Bibhas Chakraborty","doi":"10.1111/insr.12583","DOIUrl":"https://doi.org/10.1111/insr.12583","url":null,"abstract":"SummaryIn recent years, reinforcement learning (RL) has acquired a prominent position in health‐related sequential decision‐making problems, gaining traction as a valuable tool for delivering adaptive interventions (AIs). However, in part due to a poor synergy between the methodological and the applied communities, its real‐life application is still limited and its potential is still to be realised. To address this gap, our work provides the first unified technical survey on RL methods, complemented with case studies, for constructing various types of AIs in healthcare. In particular, using the common methodological umbrella of RL, we bridge two seemingly different AI domains, dynamic treatment regimes and just‐in‐time adaptive interventions in mobile health, highlighting similarities and differences between them and discussing the implications of using RL. Open problems and considerations for future research directions are outlined. Finally, we leverage our experience in designing case studies in both areas to showcase the significant collaborative opportunities between statistical, RL and healthcare researchers in advancing AIs.","PeriodicalId":14479,"journal":{"name":"International Statistical Review","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141872603","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":"Summary characteristics for multivariate function‐valued spatial point process attributes","authors":"Matthias Eckardt, Carles Comas, Jorge Mateu","doi":"10.1111/insr.12582","DOIUrl":"https://doi.org/10.1111/insr.12582","url":null,"abstract":"SummaryPrompted by modern technologies in data acquisition, the statistical analysis of spatially distributed function‐valued quantities has attracted a lot of attention in recent years. In particular, combinations of functional variables and spatial point processes yield a highly challenging instance of such modern spatial data applications. Indeed, the analysis of spatial random point configurations, where the point attributes themselves are functions rather than scalar‐valued quantities, is just in its infancy, and extensions to function‐valued quantities still remain limited. In this view, we extend current existing first‐ and second‐order summary characteristics for real‐valued point attributes to the case where, in addition to every spatial point location, a set of distinct function‐valued quantities are available. Providing a flexible treatment of more complex point process scenarios, we build a framework to consider points with multivariate function‐valued marks, and develop sets of different cross‐function (cross‐type and also multi‐function cross‐type) versions of summary characteristics that allow for the analysis of highly demanding modern spatial point process scenarios. We consider estimators of the theoretical tools and analyse their behaviour through a simulation study and two real data applications.","PeriodicalId":14479,"journal":{"name":"International Statistical Review","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141743616","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":"Controlled Epidemiological Studies Marie Reilly Chapman & Hall/CRC Biostatistics Series, CRC Press, 2023, xxxii + 439 pages, £115/$150, hardcover. ISBN: 978-0-367-18678-4 (hbk)","authors":"Reijo Sund","doi":"10.1111/insr.12586","DOIUrl":"10.1111/insr.12586","url":null,"abstract":"","PeriodicalId":14479,"journal":{"name":"International Statistical Review","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141549900","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":"Post-Shrinkage Strategies in Statistical and Machine Learning for High Dimensional Data Syed Ejaz Ahmed, Feryaal Ahmed, Bahadir Yüzbaşı. Chapman & Hall/CRC Press, 2023, 408 page, £125/$160, hardback ISBN: 9780367763442","authors":"Ravindra Khattree","doi":"10.1111/insr.12584","DOIUrl":"10.1111/insr.12584","url":null,"abstract":"","PeriodicalId":14479,"journal":{"name":"International Statistical Review","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141549818","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":"Soccer Analytics: An Introduction Using R. Clive BeggsChapman & Hall/CRC, 2024, xvi + 380 pages, £49.99, hardcover ISBN: 978-1-0323-5758-4","authors":"Antony Unwin","doi":"10.1111/insr.12585","DOIUrl":"https://doi.org/10.1111/insr.12585","url":null,"abstract":"","PeriodicalId":14479,"journal":{"name":"International Statistical Review","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141967945","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 Conversation With Marc Hallin","authors":"Christian Genest","doi":"10.1111/insr.12576","DOIUrl":"10.1111/insr.12576","url":null,"abstract":"<p>Marc Hallin was born in Ghent, Belgium, on 23 April 1949. He holds a <i>Licence en Sciences mathématiques</i> (1971), a <i>Licence en Sciences actuarielles</i> (1972), and a <i>Doctorat en Sciences</i> (1976) from the <i>Université libre de Bruxelles</i>. He then rose through the professorial ranks at the same institution, being successively <i>Premier Assistant</i> (1977–1978), <i>Chargé de Cours associé</i> (1978–1984), <i>Chargé de Cours</i> (1984–1988), <i>Professeur ordinaire</i> (1988–2009), and <i>Professeur ordinaire émérite</i> upon retirement in 2009. Throughout his career, he supervised 25 PhD students and held invited positions at many institutions of high standing in Austria, Belgium, England, France, Hong Kong, Italy, Portugal, Spain, Switzerland, and the USA (most notably Princeton). A renown expert in time series analysis, econometrics, and non-parametric inference, Marc is the author or coauthor of over 250 research papers, for which he received numerous awards, including the Medal of the Faculty of Mathematics and Physics of Charles University in Prague (2006), a <i>Humboldt Forschungspreis</i> from the Alexander von Humboldt Foundation (2012), the Pierre-Simon de Laplace Award of the <i>Société française de Statistique</i> (2022), and the Gottfried E. Noether Distinguished Scholar Award of the American Statistical Association (2022). He gave several distinguished lecture series, including the 2017 Hermann Otto Hirschfeld Lecture Series at the <i>Humboldt Universität zu Berlin</i>, and the 2018 Mahalanobis Memorial Lecture at the Indian Statistical Institute. Over the years, he co-edited a dozen books and proceedings, and served on the editorial boards of several journals, including the <i>Journal of Time Series Analysis</i> (1994–2009), the <i>Journal of Econometrics</i> (2013–2019), the <i>Journal of Business and Economic Statistics</i> (2018–), and the Theory and Methods Section of the <i>Journal of the American Statistical Association</i> (2005–). He is a Fellow of the Institute of Mathematical Statistics (1990) and the American Statistical Association (1997), as well as a member of the <i>Classe des Sciences</i> of the Royal Academy of Belgium (1999). Marc has been a member of the International Statistical Institute since 1985 and was (co-) Editor-in-Chief of the <i>International Statistical Review</i> from 2010 to 2015.</p>","PeriodicalId":14479,"journal":{"name":"International Statistical Review","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/insr.12576","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141364226","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 Two-stage Bayesian Small Area Estimation Approach for Proportions","authors":"James Hogg,&nbsp;Jessica Cameron,&nbsp;Susanna Cramb,&nbsp;Peter Baade,&nbsp;Kerrie Mengersen","doi":"10.1111/insr.12572","DOIUrl":"10.1111/insr.12572","url":null,"abstract":"<p>With the rise in popularity of digital Atlases to communicate spatial variation, there is an increasing need for robust small area estimates. However, current small area estimation methods suffer from various modelling problems when data are very sparse or when estimates are required for areas with very small populations. These issues are particularly heightened when modelling proportions. Additionally, recent work has shown significant benefits in modelling at both the individual and area levels. We propose a two-stage Bayesian hierarchical small area estimation approach for proportions that can account for survey design, reduce direct estimate instability and generate prevalence estimates for small areas with no survey data. Using a simulation study, we show that, compared with existing Bayesian small area estimation methods, our approach can provide optimal predictive performance (Bayesian mean relative root mean squared error, mean absolute relative bias and coverage) of proportions under a variety of data conditions, including very sparse and unstable data. To assess the model in practice, we compare modelled estimates of current smoking prevalence for 1,630 small areas in Australia using the 2017–2018 National Health Survey data combined with 2016 census data.</p>","PeriodicalId":14479,"journal":{"name":"International Statistical Review","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/insr.12572","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141269506","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}