International Statistical Review最新文献_第4页

IF 1.8 3区数学

International Statistical Review Pub Date : 2026-04-07 Epub Date: 2025-08-18 DOI: 10.1111/insr.70005

Sarah Leyder, Jakob Raymaekers, Peter J. Rousseeuw

引用次数: 0

Who Is My Neighbour? A Discussion of ‘A Socio-Demographic Latent Space Approach to Spatial Data When Geography Is Important but Not All-Important’ 谁是我的邻居？“当地理很重要但不是全部重要时，空间数据的社会人口潜在空间方法”的讨论

IF 1.8 3区数学

International Statistical Review Pub Date : 2025-09-02 DOI: 10.1111/insr.70011

Lance A. Waller

引用次数: 0

Invited Discussion on Article by Nandy, Holan and Schweinberger Nandy， Holan和Schweinberger文章邀请讨论

IF 1.8 3区数学

International Statistical Review Pub Date : 2025-08-18 DOI: 10.1111/insr.70008

Jonathan Bradley

引用次数: 0

Rejoinder: A Socio-Demographic Latent Space Approach to Spatial Data When Geography is Important but Not All-Important 反驳：当地理很重要但不是全部重要时，对空间数据的社会人口潜在空间方法

IF 1.8 3区数学

International Statistical Review Pub Date : 2025-08-07 DOI: 10.1111/insr.70009

Saikat Nandy, Scott H. Holan, Michael Schweinberger

引用次数: 0

A Socio-demographic Latent Space Approach to Spatial Data When Geography Is Important But not All-Important1 当地理很重要但不是全部重要时，空间数据的社会人口潜在空间方法1

IF 1.8 3区数学

International Statistical Review Pub Date : 2025-08-07 DOI: 10.1111/insr.70004

Saikat Nandy, Scott H. Holan, Michael Schweinberger

{"title":"A Socio-demographic Latent Space Approach to Spatial Data When Geography Is Important But not All-Important1","authors":"Saikat Nandy, Scott H. Holan, Michael Schweinberger","doi":"10.1111/insr.70004","DOIUrl":"https://doi.org/10.1111/insr.70004","url":null,"abstract":"<div>\u0000 \u0000 Many models for spatial and spatio-temporal data assume that ‘near things are more related than distant things’, which is known as the first law of geography. While geography may be important, it may not be all-important, for at least two reasons. First, technology helps bridge distance, so that regions separated by large distances may be more similar than would be expected based on geographical distance. Second, geographical, political and social divisions can make neighbouring regions dissimilar. We develop a flexible Bayesian approach for learning from spatial data in which units are close in an unobserved socio-demographic space and hence which units are similar. While classic approaches based on nearest-neighbour adjacency matrices may not fully capture all of the spatial correlation, the proposed approach learns neighbourhoods from data, and averages over all possible neighbourhood structures. We demonstrate the advantages of the proposed approach by presenting simulations along with applications to county-level American Community Survey data on median household income in the US states of Florida, North Carolina and South Carolina.\u0000 </div>","PeriodicalId":14479,"journal":{"name":"International Statistical Review","volume":"93 3","pages":"351-373"},"PeriodicalIF":1.8,"publicationDate":"2025-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145443127","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

Statistical Depth Meets Machine Learning: Kernel Mean Embeddings and Depth in Functional Data Analysis 统计深度满足机器学习：核均值嵌入和功能数据分析的深度

IF 1.8 3区数学

International Statistical Review Pub Date : 2025-03-16 DOI: 10.1111/insr.12611

George Wynne, Stanislav Nagy

{"title":"Statistical Depth Meets Machine Learning: Kernel Mean Embeddings and Depth in Functional Data Analysis","authors":"George Wynne, Stanislav Nagy","doi":"10.1111/insr.12611","DOIUrl":"https://doi.org/10.1111/insr.12611","url":null,"abstract":"<div>\u0000 \u0000 Statistical depth is the act of gauging how representative a point is compared with a reference probability measure. The depth allows introducing rankings and orderings to data living in multivariate, or function spaces. Though widely applied and with much experimental success, little theoretical progress has been made in analysing functional depths. This article highlights how the common \u0000<math>\u0000 <mi>h</mi></math>-depth and related depths from functional data analysis can be viewed as a kernel mean embedding, widely used in statistical machine learning. This facilitates answers to several open questions regarding the statistical properties of functional depths. We show that (i) \u0000<math>\u0000 <mi>h</mi></math>-depth has the interpretation of a kernel-based method; (ii) several \u0000<math>\u0000 <mi>h</mi></math>-depths possess explicit expressions, without the need to estimate them using Monte Carlo procedures; (iii) under minimal assumptions, \u0000<math>\u0000 <mi>h</mi></math>-depths and their maximisers are uniformly strongly consistent and asymptotically Gaussian (also in infinite-dimensional spaces and for imperfectly observed functional data); and (iv) several \u0000<math>\u0000 <mi>h</mi></math>-depths uniquely characterise probability distributions in separable Hilbert spaces. In addition, we also provide a link between the depth and empirical characteristic function based procedures for functional data. Finally, the unveiled connections enable to design an extension of the \u0000<math>\u0000 <mi>h</mi></math>-depth towards regression problems.\u0000 </div>","PeriodicalId":14479,"journal":{"name":"International Statistical Review","volume":"93 2","pages":"317-348"},"PeriodicalIF":1.8,"publicationDate":"2025-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144774096","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

Feature Screening for Ultrahigh Dimensional Mixed Data via Wasserstein Distance 基于Wasserstein距离的超高维混合数据特征筛选

IF 1.8 3区数学

International Statistical Review Pub Date : 2025-02-15 DOI: 10.1111/insr.12609

Bing Tian, Hong Wang

引用次数: 0

How Do Applied Researchers Use the Causal Forest? A Methodological Review 应用研究人员如何使用因果森林？方法回顾

IF 1.8 3区数学

International Statistical Review Pub Date : 2025-02-12 DOI: 10.1111/insr.12610

Patrick Rehill

{"title":"How Do Applied Researchers Use the Causal Forest? A Methodological Review","authors":"Patrick Rehill","doi":"10.1111/insr.12610","DOIUrl":"https://doi.org/10.1111/insr.12610","url":null,"abstract":"This methodological review examines the use of the causal forest method by applied researchers across 133 peer-reviewed papers. It shows that the emerging best practice relies heavily on the approach and tools created by the original authors of the causal forest such as their grf package and the approaches given by them in examples. Generally, researchers use the causal forest on a relatively low-dimensional dataset relying on observed controls or in some cases experiments to identify effects. There are several common ways to then communicate results–by mapping out the univariate distribution of individual-level treatment effect estimates, displaying variable importance results for the forest and graphing the distribution of treatment effects across covariates that are important either for theoretical reasons or because they have high variable importance. Some deviations from this common practice are interesting and deserve further development and use. Others are unnecessary or even harmful. The paper concludes by reflecting on the emerging best practice for causal forest use and paths for future research.","PeriodicalId":14479,"journal":{"name":"International Statistical Review","volume":"93 2","pages":"288-316"},"PeriodicalIF":1.8,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/insr.12610","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144774102","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

On the Number of Components for Matrix-Variate Mixtures: A Comparison Among Information Criteria 关于矩阵-变量混合的分量数：信息准则的比较

IF 1.8 3区数学

International Statistical Review Pub Date : 2025-01-08 DOI: 10.1111/insr.12607

Salvatore D. Tomarchio, Antonio Punzo

引用次数: 0

Chance-Corrected Interrater Agreement Statistics for Two-Rater Dichotomous Responses: A Method Review With Comparative Assessment Under Possibly Correlated Decisions 二评者二分反应的机会校正间评者一致性统计：在可能相关决策下的比较评估方法回顾

IF 1.8 3区数学

International Statistical Review Pub Date : 2025-01-06 DOI: 10.1111/insr.12606

Zizhong Tian, Vernon M. Chinchilli, Chan Shen, Shouhao Zhou

{"title":"Chance-Corrected Interrater Agreement Statistics for Two-Rater Dichotomous Responses: A Method Review With Comparative Assessment Under Possibly Correlated Decisions","authors":"Zizhong Tian, Vernon M. Chinchilli, Chan Shen, Shouhao Zhou","doi":"10.1111/insr.12606","DOIUrl":"https://doi.org/10.1111/insr.12606","url":null,"abstract":"Measurement of the interrater agreement (IRA) is critical for assessing the reliability and validity of ratings in various disciplines. While numerous IRA statistics have been developed, there is a lack of guidance on selecting appropriate measures especially when raters' decisions could be correlated. To address this gap, we review a family of chance-corrected IRA statistics for two-rater dichotomous-response cases, a fundamental setting that not only serves as the theoretical foundation for categorical-response or multirater IRA methods but is also practically dominant in most empirical studies, and we propose a novel data-generating framework to simulate correlated decision processes between raters. Subsequently, a new estimand, which calibrates the ‘true’ chance-corrected IRA, is introduced while accounting for the potential ‘probabilistic certainty’. Extensive simulations were conducted to evaluate the performance of the reviewed IRA methods under various practical scenarios and were summarised by an agglomerative hierarchical clustering analysis. Finally, we provide recommendations for selecting appropriate IRA statistics based on outcome prevalence and rater characteristics and highlight the need for further advancements in IRA estimation methodologies.","PeriodicalId":14479,"journal":{"name":"International Statistical Review","volume":"93 2","pages":"199-221"},"PeriodicalIF":1.8,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/insr.12606","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144773939","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