Pub Date : 2025-09-26DOI: 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}
Pub Date : 2025-09-23DOI: 10.1080/00031305.2025.2564268
Nicholas D. Edwards, Enzo de Jong, Feng Liu, Stephen T. Ferguson
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.
{"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}
Pub Date : 2025-09-22DOI: 10.1080/00031305.2025.2563730
Seungwoo Kang, Hee-Seok Oh
We introduce novel measures, prestige and centrality, for quantifying the prominence of each vertex in a strongly connected and directed graph by utilizing the concept of 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.
我们引入了新的度量，L1威望和L1中心性，通过利用L1数据深度的概念来量化强连接和有向图中每个顶点的突出性（Vardi和Zhang， Proc. Natl.）。学会科学。[j] .美国科学，1997(4):1423-1426,2000。前者量化每个顶点在接收选择中的突出程度，而后者评估给出选择的重要性程度。所提出的度量方法可以处理同时具有边权和顶点权的图，以及无向图。然而，使用在单一“尺度”上定义的度量来检查图，不可避免地会导致信息的丢失，因为每个顶点可能在不同的局部性水平上表现出不同的结构特征。为此，我们进一步开发了具有可调局部性参数的拟议度量的本地版本。使用这些工具，我们提出了一个多尺度网络分析框架，它提供了比单尺度检查更丰富的关于每个顶点的结构信息。通过将所提出的度量方法应用于基于首尔交通流量数据构建的网络，证明了这些度量方法准确地描述和揭示了单个城市区域的内在特征。
{"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}
Pub Date : 2025-09-19DOI: 10.1080/00031305.2025.2562891
David M. Steinberg, Geert Molenberghs, Arne C. Bathke, Ralph Brinks, Amit Huppert, Filomena Maggino, Bhramar Mukherjee
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...
{"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}
Pub Date : 2025-09-16DOI: 10.1080/00031305.2025.2561146
Don van den Bergh, Fabian Dablander
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 EqualitySampler, and we illustrate it on examples related to the comparison of means, standard deviations, and proportions.
{"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}
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.
{"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}
Pub Date : 2025-09-11DOI: 10.1080/00031305.2025.2560371
Axel Vuorinen, Emmanuelle Comets, Moreno Ursino
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.
{"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}
Pub Date : 2025-09-04DOI: 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}
Pub Date : 2025-09-04DOI: 10.1080/00031305.2025.2554737
E. del Barrio, J.A. Cuesta-Albertos, C. Matrán
{"title":"Invariant measures of disagreement with stochastic dominance*","authors":"E. del Barrio, J.A. Cuesta-Albertos, C. Matrán","doi":"10.1080/00031305.2025.2554737","DOIUrl":"https://doi.org/10.1080/00031305.2025.2554737","url":null,"abstract":"","PeriodicalId":50801,"journal":{"name":"American Statistician","volume":"22 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144995561","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}
Pub Date : 2025-09-02DOI: 10.1080/00031305.2025.2551223
Eric Dunipace, Lorenzo Trippa
{"title":"Interpretable Model Summaries Using the Wasserstein Distance","authors":"Eric Dunipace, Lorenzo Trippa","doi":"10.1080/00031305.2025.2551223","DOIUrl":"https://doi.org/10.1080/00031305.2025.2551223","url":null,"abstract":"","PeriodicalId":50801,"journal":{"name":"American Statistician","volume":"28 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144930899","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}
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