Statistical Science最新文献_第3页

Comment: Response-Adaptive Randomization in Clinical Trials: From Myths to Practical Considerations 评论:临床试验中的反应适应性随机化:从神话到实际考虑

IF 5.7 1区数学

Statistical Science Pub Date : 2023-05-01 DOI: 10.1214/23-sts865b

Yunshan Duan, P. Müller, Yuan Ji

引用次数: 0

Comment: A Quarter Century of Methodological Research in Response-Adaptive Randomization 评论:反应适应性随机化的四分之一世纪方法论研究

IF 5.7 1区数学

Statistical Science Pub Date : 2023-05-01 DOI: 10.1214/23-sts865a

A. Ivanova, W. Rosenberger

引用次数: 0

Comment: Advancing Clinical Trials with Novel Designs and Implementations. 评论：以新颖的设计和实现推进临床试验。

IF 6.6 1区数学

Statistical Science Pub Date : 2023-05-01 DOI: 10.1214/23-sts865c

Lorenzo Trippa, Yanxun Xu

引用次数: 0

Response-adaptive randomization in clinical trials: from myths to practical considerations. 临床试验中的反应适应性随机化：从神话到实际考虑。

IF 3.9 1区数学

Statistical Science Pub Date : 2023-05-01 DOI: 10.1214/22-STS865

David S Robertson, Kim May Lee, Boryana C López-Kolkovska, Sofía S Villar

引用次数: 0

Comment: Group Sequential Designs with Response-Adaptive Randomisation 评论：具有响应自适应随机的群序列设计

IF 5.7 1区数学

Statistical Science Pub Date : 2023-05-01 DOI: 10.1214/23-sts865d

C. Jennison

引用次数: 0

Distributed Bayesian Inference in Massive Spatial Data 海量空间数据中的分布式贝叶斯推理

IF 5.7 1区数学

Statistical Science Pub Date : 2023-01-01 DOI: 10.1214/22-sts868

Rajarshi Guhaniyogi, Cheng Li, T. Savitsky, Sanvesh Srivastava

{"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}

引用次数: 1

A Conversation with Stephen M. Stigler 《与斯蒂芬·m·斯蒂格勒的对话

IF 5.7 1区数学

Statistical Science Pub Date : 2023-01-01 DOI: 10.1214/22-sts878

S. Behseta, R. Kass

引用次数: 0

Can We Reliably Detect Biases that Matter in Observational Studies? 我们能可靠地发现观察性研究中重要的偏差吗?

IF 5.7 1区数学

Statistical Science Pub Date : 2023-01-01 DOI: 10.1214/23-sts882

P. Rosenbaum

引用次数: 0

Experimental Design in Marketplaces 市场实验设计

IF 5.7 1区数学

Statistical Science Pub Date : 2023-01-01 DOI: 10.1214/23-sts883

Patrick Bajari, Brian Burdick, G. Imbens, L. Masoero, James McQueen, Thomas S. Richardson, Ido M. Rosen

引用次数: 3

Conversations with Gábor J. Székely 与Gábor J.Székely的对话

IF 5.7 1区数学

Statistical Science Pub Date : 2023-01-01 DOI: 10.1214/22-sts873

Y. Gel, Edsel A. Peña, H. Wang

{"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}

引用次数: 0