{"title":"Comment: A Quarter Century of Methodological Research in Response-Adaptive Randomization","authors":"A. Ivanova, W. Rosenberger","doi":"10.1214/23-sts865a","DOIUrl":"https://doi.org/10.1214/23-sts865a","url":null,"abstract":"","PeriodicalId":51172,"journal":{"name":"Statistical Science","volume":" ","pages":""},"PeriodicalIF":5.7,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48979550","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}
David S Robertson, Kim May Lee, Boryana C López-Kolkovska, Sofía S Villar
Response-Adaptive Randomization (RAR) is part of a wider class of data-dependent sampling algorithms, for which clinical trials are typically used as a motivating application. In that context, patient allocation to treatments is determined by randomization probabilities that change based on the accrued response data in order to achieve experimental goals. RAR has received abundant theoretical attention from the biostatistical literature since the 1930's and has been the subject of numerous debates. In the last decade, it has received renewed consideration from the applied and methodological communities, driven by well-known practical examples and its widespread use in machine learning. Papers on the subject present different views on its usefulness, and these are not easy to reconcile. This work aims to address this gap by providing a unified, broad and fresh review of methodological and practical issues to consider when debating the use of RAR in clinical trials.
{"title":"Response-adaptive randomization in clinical trials: from myths to practical considerations.","authors":"David S Robertson, Kim May Lee, Boryana C López-Kolkovska, Sofía S Villar","doi":"10.1214/22-STS865","DOIUrl":"10.1214/22-STS865","url":null,"abstract":"<p><p>Response-Adaptive Randomization (RAR) is part of a wider class of data-dependent sampling algorithms, for which clinical trials are typically used as a motivating application. In that context, patient allocation to treatments is determined by randomization probabilities that change based on the accrued response data in order to achieve experimental goals. RAR has received abundant theoretical attention from the biostatistical literature since the 1930's and has been the subject of numerous debates. In the last decade, it has received renewed consideration from the applied and methodological communities, driven by well-known practical examples and its widespread use in machine learning. Papers on the subject present different views on its usefulness, and these are not easy to reconcile. This work aims to address this gap by providing a unified, broad and fresh review of methodological and practical issues to consider when debating the use of RAR in clinical trials.</p>","PeriodicalId":51172,"journal":{"name":"Statistical Science","volume":"38 2","pages":"185-208"},"PeriodicalIF":3.9,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7614644/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9647637","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}
Rajarshi Guhaniyogi, Cheng Li, T. Savitsky, Sanvesh Srivastava
Gaussian process (GP) regression is computationally expensive in spatial applications involving massive data. Various methods address this limitation, including a small number of Bayesian methods based on distributed computations (or the divide-and-conquer strategy). Focusing on the latter literature, we achieve three main goals. First, we develop an extensible Bayesian framework for distributed spatial GP regression that embeds many popular methods. The proposed framework has three steps that partition the entire data into many subsets, apply a readily available Bayesian spatial process model in parallel on all the subsets, and combine the posterior distributions estimated on all the subsets into a pseudo posterior distribution that conditions on the entire data. The combined pseudo posterior distribution replaces the full data posterior distribution in prediction and inference problems. Demonstrating our framework’s generality, we extend posterior computations for (non-distributed) spatial process models with a stationary full-rank and a nonstationary low-rank GP priors to the distributed setting. Second, we contrast the empirical performance of popular distributed approaches with some widely used non-distributed alternatives and highlight their relative advantages and shortcomings. Third, we provide theoretical support for our numerical observations and show that the Bayes L2-risks of the combined posterior distributions obtained from a subclass of the divide-and-conquer methods achieves the near-optimal convergence rate in estimating the true spatial surface with various types of covariance functions. Additionally, we provide upper bounds on the number of subsets to achieve these near-optimal rates.
{"title":"Distributed Bayesian Inference in Massive Spatial Data","authors":"Rajarshi Guhaniyogi, Cheng Li, T. Savitsky, Sanvesh Srivastava","doi":"10.1214/22-sts868","DOIUrl":"https://doi.org/10.1214/22-sts868","url":null,"abstract":"Gaussian process (GP) regression is computationally expensive in spatial applications involving massive data. Various methods address this limitation, including a small number of Bayesian methods based on distributed computations (or the divide-and-conquer strategy). Focusing on the latter literature, we achieve three main goals. First, we develop an extensible Bayesian framework for distributed spatial GP regression that embeds many popular methods. The proposed framework has three steps that partition the entire data into many subsets, apply a readily available Bayesian spatial process model in parallel on all the subsets, and combine the posterior distributions estimated on all the subsets into a pseudo posterior distribution that conditions on the entire data. The combined pseudo posterior distribution replaces the full data posterior distribution in prediction and inference problems. Demonstrating our framework’s generality, we extend posterior computations for (non-distributed) spatial process models with a stationary full-rank and a nonstationary low-rank GP priors to the distributed setting. Second, we contrast the empirical performance of popular distributed approaches with some widely used non-distributed alternatives and highlight their relative advantages and shortcomings. Third, we provide theoretical support for our numerical observations and show that the Bayes L2-risks of the combined posterior distributions obtained from a subclass of the divide-and-conquer methods achieves the near-optimal convergence rate in estimating the true spatial surface with various types of covariance functions. Additionally, we provide upper bounds on the number of subsets to achieve these near-optimal rates.","PeriodicalId":51172,"journal":{"name":"Statistical Science","volume":" ","pages":""},"PeriodicalIF":5.7,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49399980","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}
{"title":"A Conversation with Stephen M. Stigler","authors":"S. Behseta, R. Kass","doi":"10.1214/22-sts878","DOIUrl":"https://doi.org/10.1214/22-sts878","url":null,"abstract":"","PeriodicalId":51172,"journal":{"name":"Statistical Science","volume":" ","pages":""},"PeriodicalIF":5.7,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43614205","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}
{"title":"Can We Reliably Detect Biases that Matter in Observational Studies?","authors":"P. Rosenbaum","doi":"10.1214/23-sts882","DOIUrl":"https://doi.org/10.1214/23-sts882","url":null,"abstract":"","PeriodicalId":51172,"journal":{"name":"Statistical Science","volume":" ","pages":""},"PeriodicalIF":5.7,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43026263","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}
Patrick Bajari, Brian Burdick, G. Imbens, L. Masoero, James McQueen, Thomas S. Richardson, Ido M. Rosen
{"title":"Experimental Design in Marketplaces","authors":"Patrick Bajari, Brian Burdick, G. Imbens, L. Masoero, James McQueen, Thomas S. Richardson, Ido M. Rosen","doi":"10.1214/23-sts883","DOIUrl":"https://doi.org/10.1214/23-sts883","url":null,"abstract":"","PeriodicalId":51172,"journal":{"name":"Statistical Science","volume":" ","pages":""},"PeriodicalIF":5.7,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42358046","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}
. Gábor J. Székely was born in Budapest, Hungary on February 4, 1947. He graduated from Eötvös Loránd University (ELTE) with an M.S. degree in 1970, and a Ph.D. degree in 1971. He received his Candidate Degree from the Hungarian Academy of Sciences in 1976, and the Doctor of Science Degree (D. Sc.) from the Hungarian Academy of Sciences in 1986. Székely joined the Department of Probability Theory of ELTE in 1970. In 1989 he became the founding chair of the Department of Stochastics of the Budapest Institute of Technology (Technical University of Budapest). In 1995 he moved to the United States as a tenured full professor at Bowling Green State University (BGSU) in Bowling, Green, Ohio. Before that, in 1990-91, he was the first Lukacs Distinguished Professor at BGSU. Székely had several visiting positions, e.g., at the University of Amsterdam in 1976 and at Yale University in 1989. Between 2006 and 2022 he served as a Program Director in the Statistics Program of the Division of Mathematical Sciences at the US National Science Foundation. Székely has about 250 publications, including 6 books in several lan-guages. In 1988 he received the Rollo Davidson Prize from Cambridge University, jointly with Imre Z. Ruzsa for their work on algebraic probability theory. In 2010 Székely became an Elected Fellow of the Institute of Mathematical Statistics mostly for his works dealing with physics concepts in statistics like energy statistics and distance correlation. Székely was an invited speaker at several Joint Statistical Meetings and also an organizer of invited sessions on energy statistics and distance correlation. Székely was an invited speaker at the centenary of Dortmund University in Germany and also at the Institute for Advanced Studies in Princeton, New Jersey. According to Google scholar, the number of recent citations to his publications exceeds 1,200/year. He had the fortune to know and work with world-class mathematicians and
Gábor J.Székely 1947年2月4日出生于匈牙利布达佩斯。1970年毕业于罗兰大学（ELTE），获得硕士学位，1971年获得博士学位。1976年，他获得匈牙利科学院的候选学位，1986年获得匈牙利科学学院的理学博士学位。Székely于1970年加入英语教学的概率论系。1989年，他成为布达佩斯理工学院（布达佩斯技术大学）斯多葛学系的创始主席。1995年，他移居美国，在俄亥俄州格林市鲍灵的鲍灵格林州立大学（BGSU）担任终身正教授。在此之前，在1990-91年，他是BGSU的第一位卢卡奇杰出教授。Székely曾担任过多个访问职位，例如1976年在阿姆斯特丹大学和1989年在耶鲁大学。2006年至2022年间，他担任美国国家科学基金会数学科学部统计项目主任。Székely出版了大约250种出版物，其中包括6本几种语言的书。1988年，他与Imre Z.Ruzsa共同获得了剑桥大学的罗洛-戴维森奖，以表彰他们在代数概率论方面的工作。2010年，Székely成为数理统计研究所的当选研究员，主要是因为他处理统计学中的物理概念，如能量统计和距离相关性。Székely是几次联合统计会议的特邀发言人，也是能源统计和距离相关性特邀会议的组织者。Székely是德国多特蒙德大学百年校庆和新泽西州普林斯顿高等研究所的特邀演讲人。根据谷歌学者的说法，他的出版物最近被引用的次数超过1200次/年。他有幸认识并与世界级数学家和
{"title":"Conversations with Gábor J. Székely","authors":"Y. Gel, Edsel A. Peña, H. Wang","doi":"10.1214/22-sts873","DOIUrl":"https://doi.org/10.1214/22-sts873","url":null,"abstract":". Gábor J. Székely was born in Budapest, Hungary on February 4, 1947. He graduated from Eötvös Loránd University (ELTE) with an M.S. degree in 1970, and a Ph.D. degree in 1971. He received his Candidate Degree from the Hungarian Academy of Sciences in 1976, and the Doctor of Science Degree (D. Sc.) from the Hungarian Academy of Sciences in 1986. Székely joined the Department of Probability Theory of ELTE in 1970. In 1989 he became the founding chair of the Department of Stochastics of the Budapest Institute of Technology (Technical University of Budapest). In 1995 he moved to the United States as a tenured full professor at Bowling Green State University (BGSU) in Bowling, Green, Ohio. Before that, in 1990-91, he was the first Lukacs Distinguished Professor at BGSU. Székely had several visiting positions, e.g., at the University of Amsterdam in 1976 and at Yale University in 1989. Between 2006 and 2022 he served as a Program Director in the Statistics Program of the Division of Mathematical Sciences at the US National Science Foundation. Székely has about 250 publications, including 6 books in several lan-guages. In 1988 he received the Rollo Davidson Prize from Cambridge University, jointly with Imre Z. Ruzsa for their work on algebraic probability theory. In 2010 Székely became an Elected Fellow of the Institute of Mathematical Statistics mostly for his works dealing with physics concepts in statistics like energy statistics and distance correlation. Székely was an invited speaker at several Joint Statistical Meetings and also an organizer of invited sessions on energy statistics and distance correlation. Székely was an invited speaker at the centenary of Dortmund University in Germany and also at the Institute for Advanced Studies in Princeton, New Jersey. According to Google scholar, the number of recent citations to his publications exceeds 1,200/year. He had the fortune to know and work with world-class mathematicians and","PeriodicalId":51172,"journal":{"name":"Statistical Science","volume":" ","pages":""},"PeriodicalIF":5.7,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43057164","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}
{"title":"A Conversation with Mary E. Thompson","authors":"R. Rosychuk","doi":"10.1214/22-sts877","DOIUrl":"https://doi.org/10.1214/22-sts877","url":null,"abstract":"","PeriodicalId":51172,"journal":{"name":"Statistical Science","volume":" ","pages":""},"PeriodicalIF":5.7,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46879153","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}
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