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A Probabilistic View on Predictive Constructions for Bayesian Learning 贝叶斯学习预测结构的概率观
IF 5.7 1区 数学
Statistical Science Pub Date : 2022-08-14 DOI: 10.1214/23-sts884
P. Berti, E. Dreassi, F. Leisen, P. Rigo, L. Pratelli
{"title":"A Probabilistic View on Predictive Constructions for Bayesian Learning","authors":"P. Berti, E. Dreassi, F. Leisen, P. Rigo, L. Pratelli","doi":"10.1214/23-sts884","DOIUrl":"https://doi.org/10.1214/23-sts884","url":null,"abstract":"Given a sequence $X=(X_1,X_2,ldots)$ of random observations, a Bayesian forecaster aims to predict $X_{n+1}$ based on $(X_1,ldots,X_n)$ for each $nge 0$. To this end, in principle, she only needs to select a collection $sigma=(sigma_0,sigma_1,ldots)$, called ``strategy\"in what follows, where $sigma_0(cdot)=P(X_1incdot)$ is the marginal distribution of $X_1$ and $sigma_n(cdot)=P(X_{n+1}incdotmid X_1,ldots,X_n)$ the $n$-th predictive distribution. Because of the Ionescu-Tulcea theorem, $sigma$ can be assigned directly, without passing through the usual prior/posterior scheme. One main advantage is that no prior probability is to be selected. In a nutshell, this is the predictive approach to Bayesian learning. A concise review of the latter is provided in this paper. We try to put such an approach in the right framework, to make clear a few misunderstandings, and to provide a unifying view. Some recent results are discussed as well. In addition, some new strategies are introduced and the corresponding distribution of the data sequence $X$ is determined. The strategies concern generalized P'olya urns, random change points, covariates and stationary sequences.","PeriodicalId":51172,"journal":{"name":"Statistical Science","volume":" ","pages":""},"PeriodicalIF":5.7,"publicationDate":"2022-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48914546","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A Conversation with Stephen Portnoy 对话斯蒂芬波特诺伊
IF 5.7 1区 数学
Statistical Science Pub Date : 2022-08-01 DOI: 10.1214/21-sts845
Xu He, Xi-bin Shao
{"title":"A Conversation with Stephen Portnoy","authors":"Xu He, Xi-bin Shao","doi":"10.1214/21-sts845","DOIUrl":"https://doi.org/10.1214/21-sts845","url":null,"abstract":"Steve Portnoy was born in Kankakee, Illinois in 1942. He did his undergraduate studies in mathematics at Massachusetts Institute of Technology, and then earned a master’s degree and a Ph.D. degree from the statistics department at Stanford University in 1966 and 1969, respectively. Steve Portnoy has had a distinguished career and is widely recognized as a preeminent mathematical statistician. He has made pioneering and influential contributions in several areas in statistics, including asymptotic theory, robust statistics, quantile regression, and statistics in biology. He has published more than 100 research articles. He is a former co-editor of Journal of the American Statistical Association, Theory and Methods, an elected fellow of American Statistical Association (ASA), Institute of Mathematical Statistics (IMS) and American Association for the Advancement of Science (AAAS). Steve’s professional positions have included being on the faculty of the Department of Statistics at Harvard University and the University of Illinois at Urbana-Champaign for more than 30 years. He was a founding member of the Department of Statistics at the University of Illinois in 1985 and served as the division chair (1983-1985) for Statistics Program in the Mathematics department before the Statistics department was established.","PeriodicalId":51172,"journal":{"name":"Statistical Science","volume":" ","pages":""},"PeriodicalIF":5.7,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44370837","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Intention-to-Treat Comparisons in Randomized Trials 随机试验中的意向治疗比较
IF 5.7 1区 数学
Statistical Science Pub Date : 2022-08-01 DOI: 10.1214/21-sts830
R. Prentice, A. Aragaki
{"title":"Intention-to-Treat Comparisons in Randomized Trials","authors":"R. Prentice, A. Aragaki","doi":"10.1214/21-sts830","DOIUrl":"https://doi.org/10.1214/21-sts830","url":null,"abstract":"Intention-to-treat (ITT) comparisons have a central place in reporting on randomized controlled trials, though there are typically additional analyses of interest such as those making adjustments for nonadherence. In our ITT reporting of results from the Women’s Health Initiative (WHI) randomized trials we have relied primarily on highly flexible hazard ratio (Cox) regression methods. However, these methods, especially the proportional hazards special case, have been criticized for being difficult to interpret and frequently oversimplified, and for not being consistent with modern causality theories using potential outcomes. Here we address these topics and extend our use of hazard rate methods for ITT comparisons in the WHI trials.","PeriodicalId":51172,"journal":{"name":"Statistical Science","volume":" ","pages":""},"PeriodicalIF":5.7,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48775066","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 4
Computing Bayes: From Then ‘Til Now 计算贝叶斯：从那时到现在
IF 5.7 1区 数学
Statistical Science Pub Date : 2022-08-01 DOI: 10.1214/22-STS876
G. Martin, David T. Frazier, C. Robert
{"title":"Computing Bayes: From Then ‘Til Now","authors":"G. Martin, David T. Frazier, C. Robert","doi":"10.1214/22-STS876","DOIUrl":"https://doi.org/10.1214/22-STS876","url":null,"abstract":"This paper takes the reader on a journey through the history of Bayesian computation, from the 18th century to the present day. Beginning with the one-dimensional integral first confronted by Bayes in 1763, we highlight the key contributions of: Laplace, Metropolis (and, importantly, his co-authors!), Hammersley and Handscomb, and Hastings, all of which set the foundations for the computational revolution in the late 20th century -- led, primarily, by Markov chain Monte Carlo (MCMC) algorithms. A very short outline of 21st century computational methods -- including pseudo-marginal MCMC, Hamiltonian Monte Carlo, sequential Monte Carlo, and the various `approximate' methods -- completes the paper.","PeriodicalId":51172,"journal":{"name":"Statistical Science","volume":" ","pages":""},"PeriodicalIF":5.7,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42305986","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 7
Learning and Predicting from Dynamic Models for COVID-19 Patient Monitoring. 从动态模型中学习和预测 COVID-19 患者监测。
IF 3.9 1区 数学
Statistical Science Pub Date : 2022-05-01 Epub Date: 2022-05-16 DOI: 10.1214/22-sts861
Zitong Wang, Mary Grace Bowring, Antony Rosen, Brian Garibaldi, Scott Zeger, Akihiko Nishimura
{"title":"Learning and Predicting from Dynamic Models for COVID-19 Patient Monitoring.","authors":"Zitong Wang, Mary Grace Bowring, Antony Rosen, Brian Garibaldi, Scott Zeger, Akihiko Nishimura","doi":"10.1214/22-sts861","DOIUrl":"10.1214/22-sts861","url":null,"abstract":"<p><p>COVID-19 has challenged health systems to learn how to learn. This paper describes the context, methods and challenges for learning to improve COVID-19 care at one academic health center. Challenges to learning include: (1) choosing a right clinical target; (2) designing methods for accurate predictions by borrowing strength from prior patients' experiences; (3) communicating the methodology to clinicians so they understand and trust it; (4) communicating the predictions to the patient at the moment of clinical decision; and (5) continuously evaluating and revising the methods so they adapt to changing patients and clinical demands. To illustrate these challenges, this paper contrasts two statistical modeling approaches - prospective longitudinal models in common use and retrospective analogues complementary in the COVID-19 context - for predicting future biomarker trajectories and major clinical events. The methods are applied to and validated on a cohort of 1,678 patients who were hospitalized with COVID-19 during the early months of the pandemic. We emphasize graphical tools to promote physician learning and inform clinical decision making.</p>","PeriodicalId":51172,"journal":{"name":"Statistical Science","volume":"37 2","pages":"251-265"},"PeriodicalIF":3.9,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10198065/pdf/nihms-1844199.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9864796","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Interoperability of statistical models in pandemic preparedness: principles and reality. 大流行病防备中统计模型的互操作性：原则与现实。
IF 3.9 1区 数学
Statistical Science Pub Date : 2022-05-01 DOI: 10.1214/22-STS854
George Nicholson, Marta Blangiardo, Mark Briers, Peter J Diggle, Tor Erlend Fjelde, Hong Ge, Robert J B Goudie, Radka Jersakova, Ruairidh E King, Brieuc C L Lehmann, Ann-Marie Mallon, Tullia Padellini, Yee Whye Teh, Chris Holmes, Sylvia Richardson
{"title":"Interoperability of statistical models in pandemic preparedness: principles and reality.","authors":"George Nicholson, Marta Blangiardo, Mark Briers, Peter J Diggle, Tor Erlend Fjelde, Hong Ge, Robert J B Goudie, Radka Jersakova, Ruairidh E King, Brieuc C L Lehmann, Ann-Marie Mallon, Tullia Padellini, Yee Whye Teh, Chris Holmes, Sylvia Richardson","doi":"10.1214/22-STS854","DOIUrl":"10.1214/22-STS854","url":null,"abstract":"<p><p>We present <i>interoperability</i> as a guiding framework for statistical modelling to assist policy makers asking multiple questions using diverse datasets in the face of an evolving pandemic response. Interoperability provides an important set of principles for future pandemic preparedness, through the joint design and deployment of adaptable systems of statistical models for disease surveillance using probabilistic reasoning. We illustrate this through case studies for inferring and characterising spatial-temporal prevalence and reproduction numbers of SARS-CoV-2 infections in England.</p>","PeriodicalId":51172,"journal":{"name":"Statistical Science","volume":"37 2","pages":"183-206"},"PeriodicalIF":3.9,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7612804/pdf/EMS144307.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10852221","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Statistical Challenges in Tracking the Evolution of SARS-CoV-2. 追踪 SARS-CoV-2 演变的统计挑战。
IF 6.6 1区 数学
Statistical Science Pub Date : 2022-05-01 Epub Date: 2022-05-16 DOI: 10.1214/22-sts853
Lorenzo Cappello, Jaehee Kim, Sifan Liu, Julia A Palacios
{"title":"Statistical Challenges in Tracking the Evolution of SARS-CoV-2.","authors":"Lorenzo Cappello, Jaehee Kim, Sifan Liu, Julia A Palacios","doi":"10.1214/22-sts853","DOIUrl":"10.1214/22-sts853","url":null,"abstract":"<p><p>Genomic surveillance of SARS-CoV-2 has been instrumental in tracking the spread and evolution of the virus during the pandemic. The availability of SARS-CoV-2 molecular sequences isolated from infected individuals, coupled with phylodynamic methods, have provided insights into the origin of the virus, its evolutionary rate, the timing of introductions, the patterns of transmission, and the rise of novel variants that have spread through populations. Despite enormous global efforts of governments, laboratories, and researchers to collect and sequence molecular data, many challenges remain in analyzing and interpreting the data collected. Here, we describe the models and methods currently used to monitor the spread of SARS-CoV-2, discuss long-standing and new statistical challenges, and propose a method for tracking the rise of novel variants during the epidemic.</p>","PeriodicalId":51172,"journal":{"name":"Statistical Science","volume":"37 2","pages":"162-182"},"PeriodicalIF":6.6,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9409356/pdf/nihms-1829142.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9532515","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Lessons Learned from the COVID-19 Pandemic: A Statistician’s Reflection 从COVID-19大流行中吸取的教训:统计学家的反思
IF 5.7 1区 数学
Statistical Science Pub Date : 2022-05-01 DOI: 10.1214/22-sts860
Xihong Lin
{"title":"Lessons Learned from the COVID-19 Pandemic: A Statistician’s Reflection","authors":"Xihong Lin","doi":"10.1214/22-sts860","DOIUrl":"https://doi.org/10.1214/22-sts860","url":null,"abstract":"In this article, I will discuss my experience as a statistician involved in COVID-19 research in multiple capacities in the last two years, especially in the early phase of the pandemic. I will reflect on the challenges and the lessons I have learned in pandemic research regarding data collection and access, epidemic modeling and data analysis, open science and real time dissemination of research findings, implementation science, media and public communication, and partnerships between academia, government, industry and civil society. I will also make several recommendations on navigating the next stage of the pandemic and preparing for future pandemics.","PeriodicalId":51172,"journal":{"name":"Statistical Science","volume":" ","pages":""},"PeriodicalIF":5.7,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42229218","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
Data Science in a Time of Crisis: Lessons from the Pandemic 危机时期的数据科学:大流行的教训
IF 5.7 1区 数学
Statistical Science Pub Date : 2022-05-01 DOI: 10.1214/22-sts372in
C. Sabatti, John M. Chambers
{"title":"Data Science in a Time of Crisis: Lessons from the Pandemic","authors":"C. Sabatti, John M. Chambers","doi":"10.1214/22-sts372in","DOIUrl":"https://doi.org/10.1214/22-sts372in","url":null,"abstract":"The exceptional shock of the COVID-19 pandemic has brought about an equally exceptional scientific response, over a wide range of disciplines and with a spirit of collaboration and mutual support. © Institute of Mathematical Statistics, 2022","PeriodicalId":51172,"journal":{"name":"Statistical Science","volume":" ","pages":""},"PeriodicalIF":5.7,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47344888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Data, Science, and Global Disasters 数据、科学和全球灾难
IF 5.7 1区 数学
Statistical Science Pub Date : 2022-05-01 DOI: 10.1214/22-sts858
John M. Chambers
{"title":"Data, Science, and Global Disasters","authors":"John M. Chambers","doi":"10.1214/22-sts858","DOIUrl":"https://doi.org/10.1214/22-sts858","url":null,"abstract":"The spread and impact of COVID-19 have disrupted human activities and energized a response of scientific activity on a remarkable, nearly unprecedented scale. This has somewhat distracted attention from a broad range of less immediate but fundamentally more serious global threats resulting from human actions. These can be collectively labelled the anthropocene disasters. Science cannot itself prevent or mitigate them. To do so requires a global policy resolve not currently existing. When and if that resolve emerges, science will be essential for guiding action. This science will be radically data-intensive, global and inclusive. Teams will be required that include the best and most motivated individuals from all relevant scientific disciplines, plus members knowledgable about implementing likely policy recommendations. Such participants must be attracted to join and then properly supported and rewarded–not likely with current academic structures. Some insights can be gained from the recent experience with COVID-19 and the much less recent example of research at Bell Labs.","PeriodicalId":51172,"journal":{"name":"Statistical Science","volume":"1 1","pages":""},"PeriodicalIF":5.7,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66088792","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
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