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

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

Geometric Methods for Cosmological Data on the Sphere 球面上宇宙学数据的几何方法

IF 7.9 1区数学

Annual Review of Statistics and Its Application Pub Date : 2023-11-06 DOI: 10.1146/annurev-statistics-040522-093748

Javier Carrón Duque, Domenico Marinucci

{"title":"Geometric Methods for Cosmological Data on the Sphere","authors":"Javier Carrón Duque, Domenico Marinucci","doi":"10.1146/annurev-statistics-040522-093748","DOIUrl":"https://doi.org/10.1146/annurev-statistics-040522-093748","url":null,"abstract":"This review is devoted to recent developments in the statistical analysis of spherical data, strongly motivated by applications in cosmology. We start from a brief discussion of cosmological questions and motivations, arguing that most cosmological observables are spherical random fields. Then, we introduce some mathematical background on spherical random fields, including spectral representations and the construction of needlet and wavelet frames. We then focus on some specific issues, including tools and algorithms for map reconstruction (i.e., separating the different physical components that contribute to the observed field), geometric tools for testing the assumptions of Gaussianity and isotropy, and multiple testing methods to detect contamination in the field due to point sources. Although these tools are introduced in the cosmological context, they can be applied to other situations dealing with spherical data. Finally, we discuss more recent and challenging issues, such as the analysis of polarization data, which can be viewed as realizations of random fields taking values in spin fiber bundles.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":"11 3","pages":""},"PeriodicalIF":7.9,"publicationDate":"2023-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71473809","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

Stochastic Models of Rainfall 降雨的随机模型

IF 7.9 1区数学

Annual Review of Statistics and Its Application Pub Date : 2023-10-31 DOI: 10.1146/annurev-statistics-040622-023838

Paul J. Northrop

引用次数: 0

An Update on Measurement Error Modeling 测量误差建模研究进展

IF 7.9 1区数学

Annual Review of Statistics and Its Application Pub Date : 2023-10-13 DOI: 10.1146/annurev-statistics-040722-043616

Mushan Li, Yanyuan Ma

引用次数: 0

Analysis of Microbiome Data 微生物组数据分析

IF 7.9 1区数学

Annual Review of Statistics and Its Application Pub Date : 2023-10-13 DOI: 10.1146/annurev-statistics-040522-120734

Christine B. Peterson, Satabdi Saha, Kim-Anh Do

{"title":"Analysis of Microbiome Data","authors":"Christine B. Peterson, Satabdi Saha, Kim-Anh Do","doi":"10.1146/annurev-statistics-040522-120734","DOIUrl":"https://doi.org/10.1146/annurev-statistics-040522-120734","url":null,"abstract":"The microbiome represents a hidden world of tiny organisms populating not only our surroundings but also our own bodies. By enabling comprehensive profiling of these invisible creatures, modern genomic sequencing tools have given us an unprecedented ability to characterize these populations and uncover their outsize impact on our environment and health. Statistical analysis of microbiome data is critical to infer patterns from the observed abundances. The application and development of analytical methods in this area require careful consideration of the unique aspects of microbiome profiles. We begin this review with a brief overview of microbiome data collection and processing and describe the resulting data structure. We then provide an overview of statistical methods for key tasks in microbiome data analysis, including data visualization, comparison of microbial abundance across groups, regression modeling, and network inference. We conclude with a discussion and highlight interesting future directions.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":"20 16","pages":""},"PeriodicalIF":7.9,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50164711","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: 4

Distributional Regression for Data Analysis 用于数据分析的分布回归

IF 7.9 1区数学

Annual Review of Statistics and Its Application Pub Date : 2023-10-13 DOI: 10.1146/annurev-statistics-040722-053607

Nadja Klein

引用次数: 2

Role of Statistics in Detecting Misinformation: A Review of the State of the Art, Open Issues, and Future Research Directions 统计在检测错误信息中的作用:对最新技术、开放问题和未来研究方向的回顾

IF 7.9 1区数学

Annual Review of Statistics and Its Application Pub Date : 2023-10-13 DOI: 10.1146/annurev-statistics-040622-033806

Zois Boukouvalas, Allison Shafer

{"title":"Role of Statistics in Detecting Misinformation: A Review of the State of the Art, Open Issues, and Future Research Directions","authors":"Zois Boukouvalas, Allison Shafer","doi":"10.1146/annurev-statistics-040622-033806","DOIUrl":"https://doi.org/10.1146/annurev-statistics-040622-033806","url":null,"abstract":"With the evolution of social media, cyberspace has become the default medium for social media users to communicate, especially during high-impact events such as pandemics, natural disasters, terrorist attacks, and periods of political unrest. However, during such events, misinformation can spread rapidly on social media, affecting decision-making and creating social unrest. Identifying and curtailing the spread of misinformation during high-impact events are significant data challenges given the scarcity and variety of the data, the speed by which misinformation can propagate, and the fairness aspects associated with this societal problem. Recent statistical machine learning advances have shown promise for misinformation detection; however, key limitations still make this a significant challenge. These limitations relate to using representative and bias-free multimodal data and to the explainability, fairness, and reliable performance of a system that detects misinformation. In this article, we critically discuss the current state-of-the-art approaches that attempt to respond to these complex requirements and present major unsolved issues; future research directions; and the synergies among statistics, data science, and other sciences for detecting misinformation.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":"20 15","pages":""},"PeriodicalIF":7.9,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50164712","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