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On the Metricity of the Chatterjee Correlation Coefficient 论查特吉相关系数的度量性
IF 1.8 4区 数学
American Statistician Pub Date : 2025-10-10 DOI: 10.1080/00031305.2025.2571183
Flavio Chierichetti, Mirko Giacchini, Ravi Kumar
{"title":"On the Metricity of the Chatterjee Correlation Coefficient","authors":"Flavio Chierichetti, Mirko Giacchini, Ravi Kumar","doi":"10.1080/00031305.2025.2571183","DOIUrl":"https://doi.org/10.1080/00031305.2025.2571183","url":null,"abstract":"We show that the distance measure implied by the recently proposed Chatterjee coefficient of correlation can violate the triangle inequality, both in theory and in practice.","PeriodicalId":50801,"journal":{"name":"American Statistician","volume":"10 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145255058","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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
Bad estimation, good prediction: the Lasso in dense regimes 错误的估计,正确的预测:密集状态下的套索
IF 1.8 4区 数学
American Statistician Pub Date : 2025-10-08 DOI: 10.1080/00031305.2025.2569464
Andrea Bratsberg, Magne Thoresen, Jelle J. Goeman
{"title":"Bad estimation, good prediction: the Lasso in dense regimes","authors":"Andrea Bratsberg, Magne Thoresen, Jelle J. Goeman","doi":"10.1080/00031305.2025.2569464","DOIUrl":"https://doi.org/10.1080/00031305.2025.2569464","url":null,"abstract":"For high-dimensional omics data, sparsity-inducing regularization methods such as the Lasso are widely used and often yield strong predictive performance, even in settings when the assumption of sparsity is likely violated. We demonstrate that under a specific dense model, namely the high-dimensional joint latent variable model, the Lasso produces sparse prediction rules with favorable prediction error bounds, even when the underlying regression coefficient vector is not sparse at all. We further argue that this model better represents many types of omics data than sparse linear regression models. We prove that the prediction bound under this model in fact decreases with increasing number of predictors, and confirm this through simulation examples. These results highlight the need for caution when interpreting sparse prediction rules, as strong prediction accuracy of a sparse prediction rule may not imply underlying biological significance of the individual predictors.","PeriodicalId":50801,"journal":{"name":"American Statistician","volume":"22 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145241309","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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
Linear Model Estimation and Prediction for p>n p - b> n的线性模型估计与预测
IF 1.8 4区 数学
American Statistician Pub Date : 2025-09-26 DOI: 10.1080/00031305.2025.2566251
Ronald Christensen
{"title":"Linear Model Estimation and Prediction for p>n","authors":"Ronald Christensen","doi":"10.1080/00031305.2025.2566251","DOIUrl":"https://doi.org/10.1080/00031305.2025.2566251","url":null,"abstract":"","PeriodicalId":50801,"journal":{"name":"American Statistician","volume":"131 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145153780","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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
Visualizing Kendall’s τ and Hidden Structures in Ranked Data 排序数据中Kendall τ和隐藏结构的可视化
IF 1.8 4区 数学
American Statistician Pub Date : 2025-09-23 DOI: 10.1080/00031305.2025.2564268
Nicholas D. Edwards, Enzo de Jong, Feng Liu, Stephen T. Ferguson
{"title":"Visualizing Kendall’s τ and Hidden Structures in Ranked Data","authors":"Nicholas D. Edwards, Enzo de Jong, Feng Liu, Stephen T. Ferguson","doi":"10.1080/00031305.2025.2564268","DOIUrl":"https://doi.org/10.1080/00031305.2025.2564268","url":null,"abstract":"Ranked data is commonly used in research across many fields of study including medicine, biology, psychology, and economics. One common statistic used for analyzing ranked data is Kendall’s τ coefficient, a non-parametric measure of rank correlation which describes the strength of the association between two monotonic continuous or ordinal variables. While the mathematics involved in calculating Kendall's τ is well-established, there are relatively few graphing methods available to visualize the results. Here, we describe several alternative and complementary visualization methods and provide an interactive app for graphing Kendall's τ. The resulting graphs provide a visualization of rank correlation which helps display the proportion of concordant and discordant pairs. Moreover, these methods highlight other key features of the data which are not represented by Kendall's τ alone but may nevertheless be meaningful, such as longer monotonic chains and the relationship between discrete pairs of observations. We demonstrate the utility of these approaches through several examples and compare our results to other visualization methods.","PeriodicalId":50801,"journal":{"name":"American Statistician","volume":"24 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145116181","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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
L1 Prominence Measures for Directed Graphs 有向图的L1突出度量
IF 1.8 4区 数学
American Statistician Pub Date : 2025-09-22 DOI: 10.1080/00031305.2025.2563730
Seungwoo Kang, Hee-Seok Oh
{"title":"L1\u0000 Prominence Measures for Directed Graphs","authors":"Seungwoo Kang, Hee-Seok Oh","doi":"10.1080/00031305.2025.2563730","DOIUrl":"https://doi.org/10.1080/00031305.2025.2563730","url":null,"abstract":"We introduce novel measures, <span><img alt=\"\" data-formula-source='{\"type\":\"image\",\"src\":\"/cms/asset/58477584-a277-4c04-ac5f-557269e3076b/utas_a_2563730_ilm0002.gif\"}' src=\"//:0\"/></span><span><img alt=\"\" data-formula-source='{\"type\":\"mathjax\"}' src=\"//:0\"/><math display=\"inline\"><mrow><msub><mrow><mi>L</mi></mrow><mn>1</mn></msub></mrow></math></span> prestige and <span><img alt=\"\" data-formula-source='{\"type\":\"image\",\"src\":\"/cms/asset/c93dd86e-0514-4832-8df4-280f96b64919/utas_a_2563730_ilm0003.gif\"}' src=\"//:0\"/></span><span><img alt=\"\" data-formula-source='{\"type\":\"mathjax\"}' src=\"//:0\"/><math display=\"inline\"><mrow><msub><mrow><mi>L</mi></mrow><mn>1</mn></msub></mrow></math></span> centrality, for quantifying the prominence of each vertex in a strongly connected and directed graph by utilizing the concept of <span><img alt=\"\" data-formula-source='{\"type\":\"image\",\"src\":\"/cms/asset/c144ecd8-1e24-4050-afea-05ae74cae725/utas_a_2563730_ilm0004.gif\"}' src=\"//:0\"/></span><span><img alt=\"\" data-formula-source='{\"type\":\"mathjax\"}' src=\"//:0\"/><math display=\"inline\"><mrow><msub><mrow><mi>L</mi></mrow><mn>1</mn></msub></mrow></math></span> data depth (Vardi and Zhang, Proc. Natl. Acad. Sci. U.S.A. 97(4):1423–1426, 2000). The former measure quantifies the degree of prominence of each vertex in receiving choices, whereas the latter measure evaluates the degree of importance in giving choices. The proposed measures can handle graphs with both edge and vertex weights, as well as undirected graphs. However, examining a graph using a measure defined over a single ‘scale’ inevitably leads to a loss of information, as each vertex may exhibit distinct structural characteristics at different levels of locality. To this end, we further develop local versions of the proposed measures with a tunable locality parameter. Using these tools, we present a multiscale network analysis framework that provides much richer structural information about each vertex than a single-scale inspection. By applying the proposed measures to the networks constructed from the Seoul Mobility Flow Data, it is demonstrated that these measures accurately depict and uncover the inherent characteristics of individual city regions.","PeriodicalId":50801,"journal":{"name":"American Statistician","volume":"190 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145133501","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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
Moving Forward From the COVID-19 Pandemic 从COVID-19大流行中前进
IF 1.8 4区 数学
American Statistician Pub Date : 2025-09-19 DOI: 10.1080/00031305.2025.2562891
David M. Steinberg, Geert Molenberghs, Arne C. Bathke, Ralph Brinks, Amit Huppert, Filomena Maggino, Bhramar Mukherjee
{"title":"Moving Forward From the COVID-19 Pandemic","authors":"David M. Steinberg, Geert Molenberghs, Arne C. Bathke, Ralph Brinks, Amit Huppert, Filomena Maggino, Bhramar Mukherjee","doi":"10.1080/00031305.2025.2562891","DOIUrl":"https://doi.org/10.1080/00031305.2025.2562891","url":null,"abstract":"1 IntroductionThe COVID-19 outbreak was the most serious pandemic in recent decades. More than 7 million deaths have been attributed to COVID-19 (https://covid19.who.int/). The pandemic demanded di...","PeriodicalId":50801,"journal":{"name":"American Statistician","volume":"79 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145084023","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 Bayesian Multiple Comparison Adjustment Using Dirichlet Process and Beta-Binomial Model Priors 基于Dirichlet过程和β -二项模型先验的灵活贝叶斯多重比较调整
IF 1.8 4区 数学
American Statistician Pub Date : 2025-09-16 DOI: 10.1080/00031305.2025.2561146
Don van den Bergh, Fabian Dablander
{"title":"Flexible Bayesian Multiple Comparison Adjustment Using Dirichlet Process and Beta-Binomial Model Priors","authors":"Don van den Bergh, Fabian Dablander","doi":"10.1080/00031305.2025.2561146","DOIUrl":"https://doi.org/10.1080/00031305.2025.2561146","url":null,"abstract":"Researchers frequently wish to assess the equality or inequality of groups, but this poses the challenge of adequately adjusting for multiple comparisons. Statistically, all possible configurations of equality and inequality constraints can be uniquely represented as partitions of groups, where any number of groups are equal if they are in the same element of the partition. In a Bayesian framework, one can adjust for multiple comparisons by constructing a suitable prior distribution over all possible partitions. Inspired by work on variable selection in regression, we propose a class of flexible beta-binomial priors for multiple comparison adjustment. We compare this prior setup to the Dirichlet process prior suggested by Gopalan and Berry (1998) and multiple comparison adjustment methods that do not specify a prior over partitions directly. Our approach not only allows researchers to assess pairwise equality constraints but simultaneously all possible equalities among all groups. Since the space of possible partitions grows rapidly — for ten groups, there are already 115,975 possible partitions — we use a stochastic search algorithm to efficiently explore the space. Our method is implemented in the Julia package <i>EqualitySampler</i>, and we illustrate it on examples related to the comparison of means, standard deviations, and proportions.","PeriodicalId":50801,"journal":{"name":"American Statistician","volume":"64 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145072030","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 Survey on Large Language Model-based Agents for Statistics and Data Science 基于大型语言模型的统计与数据科学代理研究
IF 1.8 4区 数学
American Statistician Pub Date : 2025-09-13 DOI: 10.1080/00031305.2025.2561140
Sun Maojun, Ruijian Han, Binyan Jiang, Houduo Qi, Defeng Sun, Yancheng Yuan, Jian Huang
{"title":"A Survey on Large Language Model-based Agents for Statistics and Data Science","authors":"Sun Maojun, Ruijian Han, Binyan Jiang, Houduo Qi, Defeng Sun, Yancheng Yuan, Jian Huang","doi":"10.1080/00031305.2025.2561140","DOIUrl":"https://doi.org/10.1080/00031305.2025.2561140","url":null,"abstract":"In recent years, data science agents powered by Large Language Models (LLMs), known as “data agents,” have shown significant potential to transform the traditional data analysis paradigm. This survey provides an overview of the evolution, capabilities, and applications of LLM-based data agents, highlighting their role in simplifying complex data tasks and lowering the entry barrier for users without related expertise. We explore current trends in the design of LLM-based frameworks, detailing essential features such as planning, reasoning, reflection, multi-agent collaboration, user interface, knowledge integration, and system design, which enable agents to address data-centric problems with minimal human intervention. Furthermore, we analyze several case studies to demonstrate the practical applications of various data agents in real-world scenarios. Finally, we identify key challenges and propose future research directions to advance the development of data agents into intelligent statistical analysis software.","PeriodicalId":50801,"journal":{"name":"American Statistician","volume":"71 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145067693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 comparative analysis of Phase I dose-finding designs incorporating pharmacokinetics information 结合药代动力学信息的I期剂量寻找设计的比较分析
IF 1.8 4区 数学
American Statistician Pub Date : 2025-09-11 DOI: 10.1080/00031305.2025.2560371
Axel Vuorinen, Emmanuelle Comets, Moreno Ursino
{"title":"A comparative analysis of Phase I dose-finding designs incorporating pharmacokinetics information","authors":"Axel Vuorinen, Emmanuelle Comets, Moreno Ursino","doi":"10.1080/00031305.2025.2560371","DOIUrl":"https://doi.org/10.1080/00031305.2025.2560371","url":null,"abstract":"In early clinical trials, incorporating biological mechanisms of drug action in model-based drug development may improve Phase I success rates compared to approaches neglecting established mechanisms. Our goal is to investigate how pharmacokinetics (PK) knowledge is introduced in dose-finding methods and assess the performance of Bayesian designs incorporating PK data to estimate toxicity and robustness to misspecifications. Following a literature review, three approaches to integrate PK data into toxicity estimation were selected. The first approach assumes a normal distribution for the Area Under the Curve (AUC). The second method estimates a population PK model from longitudinal concentration data to compute the AUC for each patient. The third considers latent PK profiles to measure drug exposure. Different scenarios were implemented reflecting assumptions about the maximum tolerated dose (MTD) position and misspecifications in PK exposure measures or the PK model. Dose-finding methods were compared using the probability of correct MTD selection and the estimated probability of toxicity at each dose. PK dose-finding designs performed well in terms of accurate MTD selection and were at least as effective as a method without PK. They were robust to underlying PK model misspecification and incorrect exposure measure. Additionally, these methods can assess the dose-toxicity curve.","PeriodicalId":50801,"journal":{"name":"American Statistician","volume":"89 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145067696","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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
Near-Peer Mentoring in Data Science: A Plot for Mutual Growth 数据科学中的近同伴指导:共同成长的情节
IF 1.8 4区 数学
American Statistician Pub Date : 2025-09-04 DOI: 10.1080/00031305.2025.2550314
Chiara Sabatti, Qian Zhao
{"title":"Near-Peer Mentoring in Data Science: A Plot for Mutual Growth","authors":"Chiara Sabatti, Qian Zhao","doi":"10.1080/00031305.2025.2550314","DOIUrl":"https://doi.org/10.1080/00031305.2025.2550314","url":null,"abstract":"","PeriodicalId":50801,"journal":{"name":"American Statistician","volume":"49 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144995560","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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
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