International Statistical Review最新文献

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

Handling Out‐of‐Sample Areas to Estimate the Unemployment Rate at Local Labour Market Areas in Italy 处理样本外地区以估算意大利当地劳动力市场地区的失业率

IF 2 3区数学

International Statistical Review Pub Date : 2024-09-10 DOI: 10.1111/insr.12596

Roberto Benedetti, Federica Piersimoni, Monica Pratesi, Nicola Salvati, Thomas Suesse

引用次数: 0

On Frequency and Probability Weights: An In‐Depth Look at Duelling Weights 关于频率和概率权重：对决权重的深入探讨

IF 2 3区数学

International Statistical Review Pub Date : 2024-08-19 DOI: 10.1111/insr.12594

Tuo Lin, Ruohui Chen, Jinyuan Liu, Tsungchin Wu, Toni T. Gui, Yangyi Li, Xinyi Huang, Kun Yang, Guanqing Chen, Tian Chen, David R. Strong, Karen Messer, Xin M. Tu

{"title":"On Frequency and Probability Weights: An In‐Depth Look at Duelling Weights","authors":"Tuo Lin, Ruohui Chen, Jinyuan Liu, Tsungchin Wu, Toni T. Gui, Yangyi Li, Xinyi Huang, Kun Yang, Guanqing Chen, Tian Chen, David R. Strong, Karen Messer, Xin M. Tu","doi":"10.1111/insr.12594","DOIUrl":"https://doi.org/10.1111/insr.12594","url":null,"abstract":"SummaryProbability weights have been widely used in addressing selection bias arising from a variety of contexts. Common examples of probability weights include sampling weights, missing data weights, and propensity score weights. Frequency weights, which are used to control for varying variabilities of aggregated outcomes, are both conceptually and analytically different from probability weights. Popular software such as R, SAS and STATA support both types of weights. Many users, including professional statisticians, become bewildered when they see identical estimates, but different standard errors and ‐values when probability weights are treated as frequency weights. Some even completely ignore the difference between the two types of weights and treat them as the same. Although a large body of literature exists on each type of weights, we have found little, if any, discussion that provides head‐to‐head comparisons of the two types of weights and associated inference methods. In this paper, we unveil the conceptual and analytic differences between the two types of weights within the context of parametric and semi‐parametric generalised linear models (GLM) and discuss valid inference for each type of weights. To the best of our knowledge, this is the first paper that looks into such differences by identifying the conditions under which the two types of weights can be treated the same analytically and providing clear guidance on the appropriate statistical models and inference procedures for each type of weights. We illustrate these considerations using real study data.","PeriodicalId":14479,"journal":{"name":"International Statistical Review","volume":"32 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142182486","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

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 2 3区数学

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

Zihang Lu

引用次数: 0

Alternative Approaches for Estimating Highest‐Density Regions 估算最高密度区域的其他方法

IF 2 3区数学

International Statistical Review Pub Date : 2024-08-13 DOI: 10.1111/insr.12592

Nina Deliu, Brunero Liseo

{"title":"Alternative Approaches for Estimating Highest‐Density Regions","authors":"Nina Deliu, Brunero Liseo","doi":"10.1111/insr.12592","DOIUrl":"https://doi.org/10.1111/insr.12592","url":null,"abstract":"SummaryAmong the variety of statistical intervals, highest‐density regions (HDRs) stand out for their ability to effectively summarise a distribution or sample, unveiling its distinctive and salient features. An HDR represents the minimum size set that satisfies a certain probability coverage, and current methods for their computation require knowledge or estimation of the underlying probability distribution or density . In this work, we illustrate a broader framework for computing HDRs, which generalises the classical density quantile method. The framework is based on neighbourhood measures, that is, measures that preserve the order induced in the sample by , and include the density as a special case. We explore a number of suitable distance‐based measures, such as the ‐nearest neighbourhood distance, and some probabilistic variants based on copula models. An extensive comparison is provided, showing the advantages of the copula‐based strategy, especially in those scenarios that exhibit complex structures (e.g. multimodalities or particular dependencies). Finally, we discuss the practical implications of our findings for estimating HDRs in real‐world applications.","PeriodicalId":14479,"journal":{"name":"International Statistical Review","volume":"33 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142182503","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

Flexible Multivariate Mixture Models: A Comprehensive Approach for Modeling Mixtures of Non‐Identical Distributions 灵活的多变量混合物模型：非同一分布混合物建模的综合方法

IF 2 3区数学

International Statistical Review Pub Date : 2024-08-12 DOI: 10.1111/insr.12593

Samyajoy Pal, Christian Heumann

引用次数: 0

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

IF 2 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":"https://doi.org/10.1111/insr.12591","url":null,"abstract":"SummaryThe 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.","PeriodicalId":14479,"journal":{"name":"International Statistical Review","volume":"29 1","pages":""},"PeriodicalIF":2.0,"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