International Statistical Review最新文献

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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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

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 1.7 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

Validating an Index of Selection Bias for Proportions in Non‐Probability Samples 验证非概率样本中比例的选择偏差指数

IF 2 3区数学

International Statistical Review Pub Date : 2024-08-08 DOI: 10.1111/insr.12590

Angelina Hammon, Sabine Zinn

{"title":"Validating an Index of Selection Bias for Proportions in Non‐Probability Samples","authors":"Angelina Hammon, Sabine Zinn","doi":"10.1111/insr.12590","DOIUrl":"https://doi.org/10.1111/insr.12590","url":null,"abstract":"SummaryFast online surveys without sampling frames are becoming increasingly important in survey research. Their recruitment methods result in non‐probability samples. As the mechanism of data generation is always unknown in such samples, the problem of non‐ignorability arises making vgeneralisation of calculated statistics to the population of interest highly questionable. Sensitivity analyses provide a valuable tool to deal with non‐ignorability. They capture the impact of different sample selection mechanisms on target statistics. In 2019, Andridge and colleagues proposed an index to quantify potential (non‐ignorable) selection bias in proportions that combines the effects of different selection mechanisms. In this paper, we validate this index with an artificial non‐probability sample generated from a large empirical data set and additionally applied it to proportions estimated from data on current political attitudes arising from a real non‐probability sample selected via River sampling. We find a number of conditions that must be met for the index to perform meaningfully. When these requirements are fulfilled, the index shows an overall good performance in both of our applications in detecting and correcting present selection bias in estimated proportions. Thus, it provides a powerful measure for evaluating the robustness of results obtained from non‐probability samples.","PeriodicalId":14479,"journal":{"name":"International Statistical Review","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141933218","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

An Interview With Peter Rousseeuw 彼得-鲁塞尤访谈录

IF 2 3区数学

International Statistical Review Pub Date : 2024-08-06 DOI: 10.1111/insr.12587

Mia Hubert

{"title":"An Interview With Peter Rousseeuw","authors":"Mia Hubert","doi":"10.1111/insr.12587","DOIUrl":"https://doi.org/10.1111/insr.12587","url":null,"abstract":"SummaryPeter J. Rousseeuw is a statistician known mainly for his work on robust statistics and cluster analysis. Among his creations are least trimmed squares regression, the minimum covariance determinant estimator, the partitioning around medoids clustering method and the silhouettes graphical display. Peter obtained his PhD in 1981 following research carried out at the ETH in Zürich, Switzerland, which led to a book on influence functions. Later, he was a professor at Delft University of Technology, The Netherlands, and at the University of Antwerp, Belgium. Next, he was a researcher at Renaissance Technologies in New York for over a decade. He then returned to Belgium as a full professor at KU Leuven, until becoming emeritus in 2022. He is an elected member of the International Statistical Institute and a fellow of the Institute of Mathematical Statistics and the American Statistical Association. In the course of his career, Peter published three books and over 200 papers, together receiving over 100 000 citations. He was awarded the George Box Medal for Business and Industrial Statistics, the Research Medal of the International Federation of Classification Societies, the Frank Wilcoxon Prize, and twice the Jack Youden Prize. Recently, Peter received the 2024 ASA Noether Distinguished Scholar Award for nonparametric statistics. His former PhD students include Annick Leroy, Rik Lopuhaä, Geert Molenberghs, Christophe Croux, Mia Hubert, Stefan Van Aelst, Tim Verdonck and Jakob Raymaekers. He is the creator and sole sponsor of the Rousseeuw Prize for Statistics, which was first handed out by the King of Belgium in 2022.","PeriodicalId":14479,"journal":{"name":"International Statistical Review","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141933217","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

Reinforcement Learning in Modern Biostatistics: Constructing Optimal Adaptive Interventions 现代生物统计学中的强化学习：构建最佳自适应干预措施

IF 2 3区数学

International Statistical Review Pub Date : 2024-07-30 DOI: 10.1111/insr.12583

Nina Deliu, Joseph Jay Williams, Bibhas Chakraborty

{"title":"Reinforcement Learning in Modern Biostatistics: Constructing Optimal Adaptive Interventions","authors":"Nina Deliu, Joseph Jay Williams, Bibhas Chakraborty","doi":"10.1111/insr.12583","DOIUrl":"https://doi.org/10.1111/insr.12583","url":null,"abstract":"SummaryIn recent years, reinforcement learning (RL) has acquired a prominent position in health‐related sequential decision‐making problems, gaining traction as a valuable tool for delivering adaptive interventions (AIs). However, in part due to a poor synergy between the methodological and the applied communities, its real‐life application is still limited and its potential is still to be realised. To address this gap, our work provides the first unified technical survey on RL methods, complemented with case studies, for constructing various types of AIs in healthcare. In particular, using the common methodological umbrella of RL, we bridge two seemingly different AI domains, dynamic treatment regimes and just‐in‐time adaptive interventions in mobile health, highlighting similarities and differences between them and discussing the implications of using RL. Open problems and considerations for future research directions are outlined. Finally, we leverage our experience in designing case studies in both areas to showcase the significant collaborative opportunities between statistical, RL and healthcare researchers in advancing AIs.","PeriodicalId":14479,"journal":{"name":"International Statistical Review","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141872603","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