International Statistical Review最新文献

{"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":"74 1","pages":""},"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,&nbsp;Carles Comas,&nbsp;Jorge Mateu","doi":"10.1111/insr.12582","DOIUrl":"10.1111/insr.12582","url":null,"abstract":"<p>Prompted 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.</p>","PeriodicalId":14479,"journal":{"name":"International Statistical Review","volume":"93 1","pages":"150-178"},"PeriodicalIF":1.7,"publicationDate":"2024-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/insr.12582","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":"OA","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":"92 2","pages":"325-326"},"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":"92 2","pages":"322-323"},"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":"92 2","pages":"324"},"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":"92 2","pages":"137-159"},"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":"92 3","pages":"455-482"},"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}

{"title":"An Optimised Optional Randomised Response Technique","authors":"Kavya Pushadapu,&nbsp;Sarjinder Singh,&nbsp;Stephen A. Sedory","doi":"10.1111/insr.12581","DOIUrl":"10.1111/insr.12581","url":null,"abstract":"<div>\u0000 \u0000 <p>In this paper, we begin by reviewing the optional randomised response technique estimator (ORRTE) developed by Chaudhuri and Mukerjee for estimating the proportion of a sensitive characteristic in a population. We show that their estimator is unbiased and has smaller variance than the Warner's estimator. Then we make an attempt at developing an optimised optional randomised response technique estimator (OORRTE). The proposed OORRTE is shown to be more efficient than the ORRTE. Findings from simulation studies are discussed and interpreted for various situations. Sample sizes for the Warner's estimator, the ORRTE and the OORRTE are computed based on power analysis introduced by Ulrich, Schroter, Striegel and Simon. Finally, we include an application to real data on COVID-19 by considering it to be partially sensitive variable; that is, sensitive to some but not to others. The data used are included in the paper and the R-codes used in the simulation study are documented in online material.</p>\u0000 </div>","PeriodicalId":14479,"journal":{"name":"International Statistical Review","volume":"93 1","pages":"130-149"},"PeriodicalIF":1.7,"publicationDate":"2024-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141188933","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":"Joint Robust Variable Selection of Mean and Covariance Model via Shrinkage Methods","authors":"Yeşim Güney,&nbsp;Fulya Gokalp Yavuz,&nbsp;Olcay Arslan","doi":"10.1111/insr.12577","DOIUrl":"10.1111/insr.12577","url":null,"abstract":"<p>A valuable and robust extension of the traditional joint mean and the covariance models when data subject to outliers and/or heavy-tailed outcomes can be achieved using the joint modelling of location and scatter matrix of the multivariate t-distribution. This model encompasses three models in itself, and the number of unknown parameters in the covariance model increases quadratically with the matrix size. As a result, selecting the important variables becomes a crucial aspect to consider. In this context, the variable selection combined with the parameter estimation is considered under the normality assumption. However, because of the non-robustness of the normal distribution, the resulting estimators will be sensitive to outliers and/or heavy taildness in the data. This paper has two objectives to overcome these problems. The first is to obtain the maximum likelihood estimates of the parameters and propose an expectation-maximisation type algorithm as an alternative to the Fisher scoring algorithm in the literature. We also consider simultaneous parameter estimation and variable selection in the multivariate t-joint location and scatter matrix models. The consistency and oracle properties of the regularised estimators are also established. Simulation studies and real data analysis are provided to assess the performance of the proposed methods.</p>","PeriodicalId":14479,"journal":{"name":"International Statistical Review","volume":"93 1","pages":"102-129"},"PeriodicalIF":1.7,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/insr.12577","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141099030","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 Survey of Monte Carlo Methods for Noisy and Costly Densities With Application to Reinforcement Learning and ABC","authors":"Fernando Llorente,&nbsp;Luca Martino,&nbsp;Jesse Read,&nbsp;David Delgado-Gómez","doi":"10.1111/insr.12573","DOIUrl":"10.1111/insr.12573","url":null,"abstract":"<div>\u0000 \u0000 <p>This survey gives an overview of Monte Carlo methodologies using surrogate models, for dealing with densities that are intractable, costly, and/or noisy. This type of problem can be found in numerous real-world scenarios, including stochastic optimisation and reinforcement learning, where each evaluation of a density function may incur some computationally-expensive or even physical (real-world activity) cost, likely to give different results each time. The surrogate model does not incur this cost, but there are important trade-offs and considerations involved in the choice and design of such methodologies. We classify the different methodologies into three main classes and describe specific instances of algorithms under a unified notation. A modular scheme that encompasses the considered methods is also presented. A range of application scenarios is discussed, with special attention to the likelihood-free setting and reinforcement learning. Several numerical comparisons are also provided.</p>\u0000 </div>","PeriodicalId":14479,"journal":{"name":"International Statistical Review","volume":"93 1","pages":"18-61"},"PeriodicalIF":1.7,"publicationDate":"2024-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141062556","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}