Annual Review of Statistics and Its Application最新文献_第4页

Recent Advances in Text Analysis 文本分析的最新进展

IF 7.9 1区数学

Annual Review of Statistics and Its Application Pub Date : 2023-11-29 DOI: 10.1146/annurev-statistics-040522-022138

Zheng Tracy Ke, Pengsheng Ji, Jiashun Jin, Wanshan Li

{"title":"Recent Advances in Text Analysis","authors":"Zheng Tracy Ke, Pengsheng Ji, Jiashun Jin, Wanshan Li","doi":"10.1146/annurev-statistics-040522-022138","DOIUrl":"https://doi.org/10.1146/annurev-statistics-040522-022138","url":null,"abstract":"Text analysis is an interesting research area in data science and has various applications, such as in artificial intelligence, biomedical research, and engineering. We review popular methods for text analysis, ranging from topic modeling to the recent neural language models. In particular, we review Topic-SCORE, a statistical approach to topic modeling, and discuss how to use it to analyze the Multi-Attribute Data Set on Statisticians (MADStat), a data set on statistical publications that we collected and cleaned. The application of Topic-SCORE and other methods to MADStat leads to interesting findings. For example, we identified 11 representative topics in statistics. For each journal, the evolution of topic weights over time can be visualized, and these results are used to analyze the trends in statistical research. In particular, we propose a new statistical model for ranking the citation impacts of 11 topics, and we also build a cross-topic citation graph to illustrate how research results on different topics spread to one another. The results on MADStat provide a data-driven picture of the statistical research from 1975 to 2015, from a text analysis perspective.Expected final online publication date for the Annual Review of Statistics and Its Application, Volume 11 is March 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.","PeriodicalId":48855,"journal":{"name":"Annual Review of Statistics and Its Application","volume":"101 8","pages":""},"PeriodicalIF":7.9,"publicationDate":"2023-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138455325","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Manifold Learning: What, How, and Why 多元学习:什么，如何，为什么

IF 7.9 1区数学

Annual Review of Statistics and Its Application Pub Date : 2023-11-29 DOI: 10.1146/annurev-statistics-040522-115238

Marina Meilă, Hanyu Zhang

引用次数: 0

Maps: A Statistical View 地图:统计视图

IF 7.9 1区数学

Annual Review of Statistics and Its Application Pub Date : 2023-11-29 DOI: 10.1146/annurev-statistics-032921-040851

Lance A. Waller

{"title":"Maps: A Statistical View","authors":"Lance A. Waller","doi":"10.1146/annurev-statistics-032921-040851","DOIUrl":"https://doi.org/10.1146/annurev-statistics-032921-040851","url":null,"abstract":"Maps provide a data framework for the statistical analysis of georeferenced data observations. Since the middle of the twentieth century, the field of spatial statistics has evolved to address key inferential questions relating to spatially defined data, yet many central statistical properties do not translate to spatially indexed and spatially correlated data, and the development of statistical inference for mapped data remains an active area of research. Rather than review statistical techniques, we review the different ways the maps of georeferenced data can influence statistical analysis, focusing especially on maps as data visualization, maps as data structures, and maps as statistics themselves, i.e., summaries of underlying patterns with accompanying uncertainty. The categories provide connections to disparate literatures addressing spatial analysis including data visualization, cartography, spatial statistics, and geography. We find maps integrate spatial analysis from motivating questions, informing analytic methods, and providing context for results.Expected final online publication date for the Annual Review of Statistics and Its Application, Volume 11 is March 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.","PeriodicalId":48855,"journal":{"name":"Annual Review of Statistics and Its Application","volume":"101 6","pages":""},"PeriodicalIF":7.9,"publicationDate":"2023-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138455327","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Communication of Statistics and Evidence in Times of Crisis 危机时期的统计和证据传播

IF 7.9 1区数学

Annual Review of Statistics and Its Application Pub Date : 2023-11-29 DOI: 10.1146/annurev-statistics-040722-052011

Claudia R. Schneider, John R. Kerr, Sarah Dryhurst, John A.D. Aston

{"title":"Communication of Statistics and Evidence in Times of Crisis","authors":"Claudia R. Schneider, John R. Kerr, Sarah Dryhurst, John A.D. Aston","doi":"10.1146/annurev-statistics-040722-052011","DOIUrl":"https://doi.org/10.1146/annurev-statistics-040722-052011","url":null,"abstract":"This review provides an overview of concepts relating to the communication of statistical and empirical evidence in times of crisis, with a special focus on COVID-19. In it, we consider topics relating to both the communication of numbers, such as the role of format, context, comparisons, and visualization, and the communication of evidence more broadly, such as evidence quality, the influence of changes in available evidence, transparency, and repeated decision-making. A central focus is on the communication of the inherent uncertainties in statistical analysis, especially in rapidly changing informational environments during crises. We present relevant literature on these topics and draw connections to the communication of statistics and empirical evidence during the COVID-19 pandemic and beyond. We finish by suggesting some considerations for those faced with communicating statistics and evidence in times of crisis.Expected final online publication date for the Annual Review of Statistics and Its Application, Volume 11 is March 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.","PeriodicalId":48855,"journal":{"name":"Annual Review of Statistics and Its Application","volume":"101 7","pages":""},"PeriodicalIF":7.9,"publicationDate":"2023-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138455326","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 Brain Network Analysis 统计脑网络分析

IF 7.9 1区数学

Annual Review of Statistics and Its Application Pub Date : 2023-11-28 DOI: 10.1146/annurev-statistics-040522-020722

Sean L. Simpson, Heather M. Shappell, Mohsen Bahrami

{"title":"Statistical Brain Network Analysis","authors":"Sean L. Simpson, Heather M. Shappell, Mohsen Bahrami","doi":"10.1146/annurev-statistics-040522-020722","DOIUrl":"https://doi.org/10.1146/annurev-statistics-040522-020722","url":null,"abstract":"The recent fusion of network science and neuroscience has catalyzed a paradigm shift in how we study the brain and led to the field of brain network analysis. Brain network analyses hold great potential in helping us understand normal and abnormal brain function by providing profound clinical insight into links between system-level properties and health and behavioral outcomes. Nonetheless, methods for statistically analyzing networks at the group and individual levels have lagged behind. We have attempted to address this need by developing three complementary statistical frameworks—a mixed modeling framework, a distance regression framework, and a hidden semi-Markov modeling framework. These tools serve as synergistic fusions of statistical approaches with network science methods, providing needed analytic foundations for whole-brain network data. Here we delineate these approaches, briefly survey related tools, and discuss potential future avenues of research. We hope this review catalyzes further statistical interest and methodological development in the field.Expected final online publication date for the Annual Review of Statistics and Its Application, Volume 11 is March 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.","PeriodicalId":48855,"journal":{"name":"Annual Review of Statistics and Its Application","volume":"80 14","pages":""},"PeriodicalIF":7.9,"publicationDate":"2023-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138449678","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Relational Event Modeling 关系事件建模

IF 7.9 1区数学

Annual Review of Statistics and Its Application Pub Date : 2023-11-28 DOI: 10.1146/annurev-statistics-040722-060248

Federica Bianchi, Edoardo Filippi-Mazzola, Alessandro Lomi, Ernst C. Wit

引用次数: 0

Competing Risks: Concepts, Methods, and Software 竞争风险:概念、方法和软件

IF 7.9 1区数学

Annual Review of Statistics and Its Application Pub Date : 2023-11-22 DOI: 10.1146/annurev-statistics-040522-094556

Ronald B. Geskus

{"title":"Competing Risks: Concepts, Methods, and Software","authors":"Ronald B. Geskus","doi":"10.1146/annurev-statistics-040522-094556","DOIUrl":"https://doi.org/10.1146/annurev-statistics-040522-094556","url":null,"abstract":"The role of competing risks in the analysis of time-to-event data is increasingly acknowledged. Software is readily available. However, confusion remains regarding the proper analysis: When and how do I need to take the presence of competing risks into account? Which quantities are relevant for my research question? How can they be estimated and what assumptions do I need to make? The main quantities in a competing risks analysis are the cause-specific cumulative incidence, the cause-specific hazard, and the subdistribution hazard. We describe their nonparametric estimation, give an overview of regression models for each of these quantities, and explain their difference in interpretation. We discuss the proper analysis in relation to the type of study question, and we suggest software in R and Stata. Our focus is on competing risks analysis in medical research, but methods can equally be applied in other fields like social science, engineering, and economics.Expected final online publication date for the Annual Review of Statistics and Its Application, Volume 11 is March 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.","PeriodicalId":48855,"journal":{"name":"Annual Review of Statistics and Its Application","volume":"27 8","pages":""},"PeriodicalIF":7.9,"publicationDate":"2023-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138293820","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Interpretable Machine Learning for Discovery: Statistical Challenges and Opportunities 用于发现的可解释机器学习:统计学的挑战和机遇

IF 7.9 1区数学

Annual Review of Statistics and Its Application Pub Date : 2023-11-17 DOI: 10.1146/annurev-statistics-040120-030919

Genevera I. Allen, Luqin Gan, Lili Zheng

{"title":"Interpretable Machine Learning for Discovery: Statistical Challenges and Opportunities","authors":"Genevera I. Allen, Luqin Gan, Lili Zheng","doi":"10.1146/annurev-statistics-040120-030919","DOIUrl":"https://doi.org/10.1146/annurev-statistics-040120-030919","url":null,"abstract":"New technologies have led to vast troves of large and complex data sets across many scientific domains and industries. People routinely use machine learning techniques not only to process, visualize, and make predictions from these big data, but also to make data-driven discoveries. These discoveries are often made using interpretable machine learning, or machine learning models and techniques that yield human-understandable insights. In this article, we discuss and review the field of interpretable machine learning, focusing especially on the techniques, as they are often employed to generate new knowledge or make discoveries from large data sets. We outline the types of discoveries that can be made using interpretable machine learning in both supervised and unsupervised settings. Additionally, we focus on the grand challenge of how to validate these discoveries in a data-driven manner, which promotes trust in machine learning systems and reproducibility in science. We discuss validation both from a practical perspective, reviewing approaches based on data-splitting and stability, as well as from a theoretical perspective, reviewing statistical results on model selection consistency and uncertainty quantification via statistical inference. Finally, we conclude by highlighting open challenges in using interpretable machine learning techniques to make discoveries, including gaps between theory and practice for validating data-driven discoveries.Expected final online publication date for the Annual Review of Statistics and Its Application, Volume 11 is March 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.","PeriodicalId":48855,"journal":{"name":"Annual Review of Statistics and Its Application","volume":"59 9","pages":""},"PeriodicalIF":7.9,"publicationDate":"2023-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138293187","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Causal Inference in the Social Sciences 社会科学中的因果推理

IF 7.9 1区数学

Annual Review of Statistics and Its Application Pub Date : 2023-11-17 DOI: 10.1146/annurev-statistics-033121-114601

Guido W. Imbens

引用次数: 1

Distributed Computing and Inference for Big Data 面向大数据的分布式计算与推理

IF 7.9 1区数学

Annual Review of Statistics and Its Application Pub Date : 2023-11-17 DOI: 10.1146/annurev-statistics-040522-021241

Ling Zhou, Ziyang Gong, Pengcheng Xiang

{"title":"Distributed Computing and Inference for Big Data","authors":"Ling Zhou, Ziyang Gong, Pengcheng Xiang","doi":"10.1146/annurev-statistics-040522-021241","DOIUrl":"https://doi.org/10.1146/annurev-statistics-040522-021241","url":null,"abstract":"Data are distributed across different sites due to computing facility limitations or data privacy considerations. Conventional centralized methods—those in which all datasets are stored and processed in a central computing facility—are not applicable in practice. Therefore, it has become necessary to develop distributed learning approaches that have good inference or predictive accuracy while remaining free of individual data or obeying policies and regulations to protect privacy. In this article, we introduce the basic idea of distributed learning and conduct a selected review on various distributed learning methods, which are categorized by their statistical accuracy, computational efficiency, heterogeneity, and privacy. This categorization can help evaluate newly proposed methods from different aspects. Moreover, we provide up-to-date descriptions of the existing theoretical results that cover statistical equivalency and computational efficiency under different statistical learning frameworks. Finally, we provide existing software implementations and benchmark datasets, and we discuss future research opportunities.Expected final online publication date for the Annual Review of Statistics and Its Application, Volume 11 is March 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.","PeriodicalId":48855,"journal":{"name":"Annual Review of Statistics and Its Application","volume":"59 7","pages":""},"PeriodicalIF":7.9,"publicationDate":"2023-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138293189","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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