Statistical Science最新文献_第3页

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

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

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

A Conversation with Mary E. Thompson 玛丽·e·汤普森对话

IF 5.7 1区数学

Statistical Science Pub Date : 2023-01-01 DOI: 10.1214/22-sts877

R. Rosychuk

引用次数: 0

The Secret Life of I. J. Good I. J.古德的秘密生活

IF 5.7 1区数学

Statistical Science Pub Date : 2023-01-01 DOI: 10.1214/22-sts870

S. Zabell

引用次数: 0

Rejoinder: Confidence as Likelihood 复辩状：信心是可能的

IF 5.7 1区数学

Statistical Science Pub Date : 2022-11-01 DOI: 10.1214/22-sts869

Y. Pawitan, Youngjo Lee

引用次数: 0

A Conversation with David J. Aldous 《与大卫·j·奥多斯的对话

IF 5.7 1区数学

Statistical Science Pub Date : 2022-11-01 DOI: 10.1214/22-sts849

S. Bhamidi

{"title":"A Conversation with David J. Aldous","authors":"S. Bhamidi","doi":"10.1214/22-sts849","DOIUrl":"https://doi.org/10.1214/22-sts849","url":null,"abstract":". David John Aldous was born in Exeter U.K. on July 13, 1952. He received a B.A. and Ph.D. in Mathematics in 1973 and 1977, respectively from Cambridge. After spending two years as a research fellow at St. John’s College, Cambridge, he joined the Department of Statistics at the University of California, Berkeley in 1979 where he spent the rest of his academic career until retiring in 2018. He is known for seminal contributions on many topics within probability including weak convergence and tightness, exchangeability, Markov chain mixing times, Poisson clumping heuristic and limit theory for large discrete random structures including random trees, stochastic coagulation and fragmentation systems, models of complex networks and interacting particle systems on such structures. For his contributions to the ﬁeld, he has received numerous honors and awards including the Rollo David-son prize in 1980, the inaugural Loeve prize in Probability in 1993, and the Brouwer medal in 2021, and being elected as an IMS fellow in 1985, Fellow of the Royal Society in 1994, Fellow of the American Academy of Arts and Sciences in 2004, elected to the National Academy of Sciences (foreign associate) in 2010, ICM plenary speaker in 2010 and AMS fellow in 2012.","PeriodicalId":51172,"journal":{"name":"Statistical Science","volume":" ","pages":""},"PeriodicalIF":5.7,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44257148","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

The Covariate-Adjusted ROC Curve: The Concept and Its Importance, Review of Inferential Methods, and a New Bayesian Estimator 协变量校正ROC曲线:概念及其重要性，推理方法综述，以及一种新的贝叶斯估计量

IF 5.7 1区数学

Statistical Science Pub Date : 2022-11-01 DOI: 10.1214/21-sts839

Vanda Inácio, M. Rodríguez-Álvarez

{"title":"The Covariate-Adjusted ROC Curve: The Concept and Its Importance, Review of Inferential Methods, and a New Bayesian Estimator","authors":"Vanda Inácio, M. Rodríguez-Álvarez","doi":"10.1214/21-sts839","DOIUrl":"https://doi.org/10.1214/21-sts839","url":null,"abstract":"Accurate diagnosis of disease is of fundamental importance in clinical practice and medical research. Before a medical diagnostic test is routinely used in practice, its ability to distinguish between diseased and nondiseased states must be rigorously assessed. The receiver operating characteristic (ROC) curve is the most popular used tool for evaluating the diagnostic accuracy of continuous-outcome tests. It has been acknowledged that several factors (e.g., subject-specific characteristics such as age and/or gender) can affect the test outcomes and accuracy beyond disease status. Recently, the covariate-adjusted ROC curve has been proposed and successfully applied as a global summary measure of diagnostic accuracy that takes covariate information into account. The aim of this paper is three-fold. First, we motivate the importance of including covariate-information, whenever available, in ROC analysis and, in particular, how the covariate-adjusted ROC curve is an important tool in this context. Second, we review and provide insight on the existing approaches for estimating the covariate-adjusted ROC curve. Third, we develop a highly flexible Bayesian method, based on the combination of a Dirichlet process mixture of additive normal models and the Bayesian bootstrap, for conducting inference about the covariate-adjusted ROC curve. A simulation study is conducted to assess the performance of the different methods and it also demonstrates the ability of our proposed Bayesian model to successfully recover the true covariate-adjusted ROC curve and to produce valid inferences in a variety of complex scenarios. The methods are applied to an endocrine study where the goal is to assess the accuracy of the body mass index, adjusted for age and gender, for detecting clusters of cardiovascular disease risk factors. key words: Classification accuracy; Covariate-adjustment; Decision threshold; Diagnostic test; Dirichlet process (mixture) model; Receiver operating characteristic curve. Vanda Inácio, School of Mathematics, University of Edinburgh, Scotland, UK (vanda.inacio@ed.ac.uk). Maŕıa Xosé RodŕıguezÁlvarez, BCAM-Basque Center for Applied Mathematics & IKERBASQUE, Basque Foundation for Science, Bilbao, Basque Country, Spain (mxrodriguez@bcamath.org).","PeriodicalId":51172,"journal":{"name":"Statistical Science","volume":" ","pages":""},"PeriodicalIF":5.7,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43020500","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: 5