International Statistical Review最新文献_第5页

Revisiting Estimation of Number of Trials in Binomial Distribution 二项分布试验数估计再认识

IF 1.8 3区数学

International Statistical Review Pub Date : 2025-01-06 DOI: 10.1111/insr.12608

Mina Georgieva, Brani Vidakovic

{"title":"Revisiting Estimation of Number of Trials in Binomial Distribution","authors":"Mina Georgieva, Brani Vidakovic","doi":"10.1111/insr.12608","DOIUrl":"https://doi.org/10.1111/insr.12608","url":null,"abstract":"<div>\u0000 \u0000 <p>Estimating the parameter \u0000<span></span><math>\u0000 <mi>n</mi></math> when \u0000<span></span><math>\u0000 <mi>p</mi></math> is known or simultaneous estimation of \u0000<span></span><math>\u0000 <mi>n</mi></math> and \u0000<span></span><math>\u0000 <mi>p</mi></math> of the binomial distribution based on \u0000<span></span><math>\u0000 <mi>k</mi>\u0000 <mo>≥</mo>\u0000 <mn>1</mn></math> independent observations has been considered by many authors over the last several decades. A range of estimators have been proposed, and questions regarding asymptotic and small sample properties received adequate treatment. In this paper, we provide an extensive review and a comprehensive performance comparison of the estimators from the literature. We propose a conceptually simple estimator of \u0000<span></span><math>\u0000 <mi>n</mi></math> that uses the marginal likelihood when \u0000<span></span><math>\u0000 <mi>p</mi></math> is integrated out by simultaneous optimisation w.r.t. \u0000<span></span><math>\u0000 <mi>n</mi></math> and the hyperparameters. We compare the proposed estimator with various existing estimators and find its performance competitive and, in some scenarios, superior.</p>\u0000 </div>","PeriodicalId":14479,"journal":{"name":"International Statistical Review","volume":"93 2","pages":"246-266"},"PeriodicalIF":1.8,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144773904","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}

引用次数: 0

A Conversation With Amy Racine-Poon 与Amy Racine-Poon的对话

IF 1.8 3区数学

International Statistical Review Pub Date : 2025-01-06 DOI: 10.1111/insr.12605

Oleksandr Sverdlov

{"title":"A Conversation With Amy Racine-Poon","authors":"Oleksandr Sverdlov","doi":"10.1111/insr.12605","DOIUrl":"https://doi.org/10.1111/insr.12605","url":null,"abstract":"<div>\u0000 \u0000 <p><b>Professor Dr. Amy Racine-Poon is best known for her interdisciplinary contributions as an applied Bayesian statistician in the pharmaceutical industry and healthcare. She was born in Hong Kong and obtained a BA with upper honors in Mathematics (1970) from the Chinese University of Hong Kong. She earned a PhD in statistics from the University of California, Berkeley, under the supervision of Erich L. Lehmann. She worked as a Lecturer at the Department of Statistics at UC Berkeley (1975–1977) and as a Statistician at the Biometry Branch of the National Institute of Environmental Health in Research Triangle Park, North Carolina (1977–1980). Amy moved to Basel, Switzerland, in 1981 to join Ciba-Geiby/Novartis AG, where she worked for 42 years (1981–2023) across different therapeutic areas and stages of drug development, applying her skills in advanced statistical and pharmacometric methodologies that led to the development of large number of new drugs. During her career, she was also a Visiting Professor at the Department of Mathematics, Imperial College London (1995–1997) and a Volunteer Statistical Expert at Bill & Melinda Gates Foundation, Seattle, Washington (2015–2019). Amy Racine-Poon's numerous honors include the Royal Statistical Society Greenfield Industrial Medal for Innovative Use of Statistics in the Industries (1995), Fellow of the American Statistical Association (1997), Novartis Distinguished Scientist Award (1999), American Statistical Association Youden Interlaboratory Research Award (2020) and the Sheiner-Beal Pharmacometrics Award (2024) from the American Society of Clinical Pharmacology and Therapeutics. The following conversation took place between Oleksandr Sverdlov (Alex) and Amy Racine-Poon (Amy) in October 2024.</b></p>\u0000 </div>","PeriodicalId":14479,"journal":{"name":"International Statistical Review","volume":"93 2","pages":"183-198"},"PeriodicalIF":1.8,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144773905","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}

引用次数: 0

Statistics: Multivariate Data Integration Using R; Methods and Applications With the mixOmics Package Kim-Anh Lê Cao, Zoe Marie WelhamChapman & Hall/CRC, 2021, xxi + 308 pages, £84.99/$115.00, hardcover ISBN: 978-1032128078 eBook ISBN: 9781003026860 统计学：使用 R 进行多变量数据整合；使用 mixOmics 软件包的方法和应用 Kim-Anh Lê Cao、Zoe Marie WelhamChapman & Hall/CRC，2021 年，xxi + 308 页，84.99 英镑/115.00 美元，精装 ISBN：978-1032128078 电子书 ISBN：9781003026860

IF 1.7 3区数学

International Statistical Review Pub Date : 2024-10-20 DOI: 10.1111/insr.12599

Krzysztof Podgórski

引用次数: 0

Philosophies, Puzzles, and Paradoxes: A Statistician's Search for the Truth Yudi Pawitan and Youngjo LeeChapman & Hall/CRC, 2024, xiv + 351 pages, £18.39/$23.96 paperback, £104/$136 hardback, £17.24/$22.46 eBook ISBN: 9781032377391 paperback; 9781032377407 hardback; 9781003341659 ebook 哲学、谜题和悖论：一位统计学家对真理的探索 Yudi Pawitan 和 Youngjo LeeChapman & Hall/CRC, 2024, xiv + 351 页，平装本 18.39 英镑/23.96 美元，精装本 104 英镑/136 美元，电子书 17.24 英镑/22.46 美元 ISBN: 9781032377391 平装本; 9781032377407 精装本; 9781003341659 电子书

IF 1.7 3区数学

International Statistical Review Pub Date : 2024-10-07 DOI: 10.1111/insr.12601

John Maindonald

引用次数: 0

Machine Learning Theory and Applications: Hands-On Use Cases With Python on Classical and Quantum Machines, Xavier Vasques, John Wiley & Sons, 2024, xx + 487 pages, $89.95, hardcover ISBN: 978-1-394-22061-8 机器学习理论与应用：使用 Python 在经典和量子机器上的实践案例》，Xavier Vasques 著，约翰-威利父子出版社，2024 年，xx + 487 页，89.95 美元，精装 ISBN：978-1-394-22061-8

IF 1.7 3区数学

International Statistical Review Pub Date : 2024-10-07 DOI: 10.1111/insr.12602

Shuangzhe Liu

引用次数: 0

Object Oriented Data Analysis J. S. Marron and I. L. DrydenChapman & Hall/CRC, 2022, xii + 424 pages, softcover ISBN: 978-0-8153-9282-8 (hbk) ISBN: 978-1-032-11480-4 (pbk) ISBN: 978-1-351-18967-5 (ebk) 面向对象的数据分析 J. S. Marron 和 I. L. DrydenChapman & Hall/CRC, 2022, xii + 424 页，软装 ISBN: 978-0-8153-9282-8 (hbk) ISBN: 978-1-032-11480-4 (pbk) ISBN: 978-1-351-18967-5 (ebk)

IF 1.7 3区数学

International Statistical Review Pub Date : 2024-10-07 DOI: 10.1111/insr.12600

Debashis Ghosh

引用次数: 0

Data Analytics and Machine Learning: Navigating the Big Data Landscape Edited by Pushpa Singh, Asha Rani Mishra, and Payal GargSpringer, 2024, 366 pages, $169.99, hardcover ISBN: 978-9819704477 《数据分析与机器学习：大数据导航》，Pushpa Singh, Asha Rani Mishra， Payal GargSpringer主编，2024,366页，169.99美元，精装ISBN: 978-9819704477

IF 1.7 3区数学

International Statistical Review Pub Date : 2024-10-07 DOI: 10.1111/insr.12603

Brian W. Sloboda

引用次数: 0

Clustering Longitudinal Data: A Review of Methods and Software Packages 纵向数据聚类：方法和软件包综述

IF 1.8 3区数学

International Statistical Review Pub Date : 2024-08-13 DOI: 10.1111/insr.12588

Zihang Lu

{"title":"Clustering Longitudinal Data: A Review of Methods and Software Packages","authors":"Zihang Lu","doi":"10.1111/insr.12588","DOIUrl":"10.1111/insr.12588","url":null,"abstract":"<p>Clustering of longitudinal data is becoming increasingly popular in many fields such as social sciences, business, environmental science, medicine and healthcare. However, it is often challenging due to the complex nature of the data, such as dependencies between observations collected over time, missingness, sparsity and non-linearity, making it difficult to identify meaningful patterns and relationships among the data. Despite the increasingly common application of cluster analysis for longitudinal data, many existing methods are still less known to researchers, and limited guidance is provided in choosing between methods and software packages. In this paper, we review several commonly used methods for clustering longitudinal data. These methods are broadly classified into three categories, namely, model-based approaches, algorithm-based approaches and functional clustering approaches. We perform a comparison among these methods and their corresponding R software packages using real-life datasets and simulated datasets under various conditions. Findings from the analyses and recommendations for using these approaches in practice are discussed.</p>","PeriodicalId":14479,"journal":{"name":"International Statistical Review","volume":"93 3","pages":"425-458"},"PeriodicalIF":1.8,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/insr.12588","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142182504","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}

引用次数: 0

Statistical Analysis of Data Repeatability Measures 数据重复性测量的统计分析

IF 1.8 3区数学

International Statistical Review Pub Date : 2024-08-09 DOI: 10.1111/insr.12591

Zeyi Wang, Eric Bridgeford, Shangsi Wang, Joshua T. Vogelstein, Brian Caffo

{"title":"Statistical Analysis of Data Repeatability Measures","authors":"Zeyi Wang, Eric Bridgeford, Shangsi Wang, Joshua T. Vogelstein, Brian Caffo","doi":"10.1111/insr.12591","DOIUrl":"10.1111/insr.12591","url":null,"abstract":"<div>\u0000 \u0000 <p>The advent of modern data collection and processing techniques has seen the size, scale and complexity of data grow exponentially. A seminal step in leveraging these rich datasets for downstream inference is understanding the characteristics of the data which are repeatable—the aspects of the data that are able to be identified under duplicated analyses. Conflictingly, the utility of traditional repeatability measures, such as the intra-class correlation coefficient, under these settings is limited. In recent work, novel data repeatability measures have been introduced in the context where a set of subjects are measured twice or more, including: fingerprinting, rank sums and generalisations of the intra-class correlation coefficient. However, the relationships between, and the best practices among, these measures remains largely unknown. In this manuscript, we formalise a novel repeatability measure, discriminability. We show that it is deterministically linked with the intra-class correlation coefficients under univariate random effect models and has the desired property of optimal accuracy for inferential tasks using multivariate measurements. Additionally, we overview and systematically compare existing repeatability statistics with discriminability, using both theoretical results and simulations. We show that the rank sum statistic is deterministically linked to a consistent estimator of discriminability. The statistical power of permutation tests derived from these measures are compared numerically under Gaussian and non-Gaussian settings, with and without simulated batch effects. Motivated by both theoretical and empirical results, we provide methodological recommendations for each benchmark setting to serve as a resource for future analyses. We believe these recommendations will play an important role towards improving repeatability in fields such as functional magnetic resonance imaging, genomics, pharmacology and more.</p>\u0000 </div>","PeriodicalId":14479,"journal":{"name":"International Statistical Review","volume":"93 3","pages":"517-543"},"PeriodicalIF":1.8,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141933273","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}

引用次数: 0

New Scheme of Empirical Likelihood Method for Ranked Set Sampling: Applications to Two One-Sample Problems 排序集抽样的经验似然法新方案：两个单样本问题的应用

IF 1.8 3区数学

International Statistical Review Pub Date : 2024-08-08 DOI: 10.1111/insr.12589

Soohyun Ahn, Xinlei Wang, Chul Moon, Johan Lim

引用次数: 0