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Annual Review of Statistics and Its Application最新文献

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Optimal Designs for Correlated Data 关联数据的优化设计
IF 7.9 1区 数学
Annual Review of Statistics and Its Application Pub Date : 2025-11-07 DOI: 10.1146/annurev-statistics-042324-012947
J. López-Fidalgo, W.K. Wong
{"title":"Optimal Designs for Correlated Data","authors":"J. López-Fidalgo, W.K. Wong","doi":"10.1146/annurev-statistics-042324-012947","DOIUrl":"https://doi.org/10.1146/annurev-statistics-042324-012947","url":null,"abstract":"Correlated data occur naturally and frequently in small and big data, and methods for analyzing correlated data have seen great advances in recent decades. Attention to design issues typically lags behind that paid to estimation issues, and it is also true that construction of optimal designs for models with correlated data lags that for models with uncorrelated data. A key problem in constructing optimal designs for models with correlated observations is that more technical assumptions are needed than when models have uncorrelated errors. In the former case, approximations to the information matrix are needed, and there are also no general and effective algorithms for finding various types of optimal designs. In addition, there are no tools to confirm optimality of a design. This article first gives a short review of optimal designs for linear models, before we focus on a review of finding optimal designs for models with correlated data. We discuss various approaches and their difficulties in a few selected areas. Along the way, we provide examples and recommend use of nature-inspired metaheuristic algorithms to find all kinds of optimal designs for any criterion or model with or without correlated data.","PeriodicalId":48855,"journal":{"name":"Annual Review of Statistics and Its Application","volume":"21 1","pages":""},"PeriodicalIF":7.9,"publicationDate":"2025-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145472687","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
The Natural Value of Treatment and Its Importance for Causal Inference 治疗的自然价值及其对因果推理的重要性
IF 7.9 1区 数学
Annual Review of Statistics and Its Application Pub Date : 2025-11-07 DOI: 10.1146/annurev-statistics-042424-110756
Aaron L. Sarvet, Mats J. Stensrud
{"title":"The Natural Value of Treatment and Its Importance for Causal Inference","authors":"Aaron L. Sarvet, Mats J. Stensrud","doi":"10.1146/annurev-statistics-042424-110756","DOIUrl":"https://doi.org/10.1146/annurev-statistics-042424-110756","url":null,"abstract":"The natural treatment value (NTV) is the value a treatment takes when it is not altered by an intervention. This observable random variable is foundational to statistical causal inference. On the one hand, identification hinges on our substantive knowledge about the NTV. On the other hand, the NTV is a defining feature of canonical estimands, like the average treatment effect in the treated, local average treatment effects, and natural effects in mediation analysis. In this article, we argue why an explicit and formal consideration of the NTV is important in statistics and related fields, and describe its role in guiding statistical analysis, formulating identification conditions, falsifying assumptions, and relating different estimands. We also review a growing literature studying estimands explicitly defined by the NTV. This allows us to highlight a subtle, often-overlooked identification issue that challenges the study of dynamic regimes that depend on the NTV. Finally, we illustrate how NTV parameters are often motivated by pragmatic concerns, and we consider the practical relevance of some of these estimands.","PeriodicalId":48855,"journal":{"name":"Annual Review of Statistics and Its Application","volume":"52 1","pages":""},"PeriodicalIF":7.9,"publicationDate":"2025-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145472685","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
Statistics for Animal Tracking Data 动物追踪数据统计
IF 7.9 1区 数学
Annual Review of Statistics and Its Application Pub Date : 2025-11-06 DOI: 10.1146/annurev-statistics-112723-034603
Vianey Leos-Barajas, Ignacio Alvarez-Castro, Juan M. Morales
{"title":"Statistics for Animal Tracking Data","authors":"Vianey Leos-Barajas, Ignacio Alvarez-Castro, Juan M. Morales","doi":"10.1146/annurev-statistics-112723-034603","DOIUrl":"https://doi.org/10.1146/annurev-statistics-112723-034603","url":null,"abstract":"Advances in technology are paving the way for researchers to remotely track wild animals and collect massive, high-resolution animal movement data sets with temporal and/or spatial structure. However, the rate at which data are becoming available is outpacing the development of statistical methodology that can adequately analyze them. In this article, we cover the most widely used modeling approaches for the analysis of animal movement data and various extensions that have been proposed for each modeling framework, as well as challenges that remain. There are several newer statistical challenges that researchers have tried to tackle in recent years, such as modeling data streams collected at vastly different temporal resolutions from multiple devices to study animal behavior and incorporating physiological processes as drivers of animal movement. We conclude with additional statistical challenges and opportunities that remain to advance the study of animal movement.","PeriodicalId":48855,"journal":{"name":"Annual Review of Statistics and Its Application","volume":"74 1","pages":""},"PeriodicalIF":7.9,"publicationDate":"2025-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145455262","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
Structure Assessment in Count Time Series 计数时间序列中的结构评估
IF 7.9 1区 数学
Annual Review of Statistics and Its Application Pub Date : 2025-10-29 DOI: 10.1146/annurev-statistics-042424-114518
Šárka Hudecová
{"title":"Structure Assessment in Count Time Series","authors":"Šárka Hudecová","doi":"10.1146/annurev-statistics-042424-114518","DOIUrl":"https://doi.org/10.1146/annurev-statistics-042424-114518","url":null,"abstract":"Assessing the performance of an estimated model for count time series is critical for subsequent statistical inference and distributional forecasting. This article reviews the most commonly used count time series models and focuses on evaluating their goodness of fit. Various formal statistical tests are presented, along with useful graphical diagnostic tools. The methods are illustrated on a real data example.","PeriodicalId":48855,"journal":{"name":"Annual Review of Statistics and Its Application","volume":"113 1","pages":""},"PeriodicalIF":7.9,"publicationDate":"2025-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145397498","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
Analysis of Tensor Time Series 张量时间序列分析
IF 7.9 1区 数学
Annual Review of Statistics and Its Application Pub Date : 2025-10-14 DOI: 10.1146/annurev-statistics-042424-063308
Stevenson Bolivar, Shuo-Chieh Huang, Rong Chen
{"title":"Analysis of Tensor Time Series","authors":"Stevenson Bolivar, Shuo-Chieh Huang, Rong Chen","doi":"10.1146/annurev-statistics-042424-063308","DOIUrl":"https://doi.org/10.1146/annurev-statistics-042424-063308","url":null,"abstract":"This article provides a comprehensive overview of statistical methods developed for the analysis of tensor time series data, which have become increasingly prevalent across various fields such as economics, finance, biology, engineering, and the social sciences. The review focuses on three primary approaches: autoregressive modeling, factor modeling, and segmentation approaches. These methods leverage the inherent tensor structure to offer advantages such as dimension reduction, enhanced interpretability, and computational efficiency. The review focuses on model settings and their potential interpretations, discussing various estimation techniques for these models and their associated theoretical properties. In addition, we outline various applications using these models and discuss potential directions for future developments.","PeriodicalId":48855,"journal":{"name":"Annual Review of Statistics and Its Application","volume":"27 1","pages":""},"PeriodicalIF":7.9,"publicationDate":"2025-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145289224","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
Regression Models with Interval-Censored Variables 区间截尾变量回归模型
IF 7.9 1区 数学
Annual Review of Statistics and Its Application Pub Date : 2025-10-14 DOI: 10.1146/annurev-statistics-042424-103337
Guadalupe Gómez Melis, Ramon Oller, Klaus Langohr
{"title":"Regression Models with Interval-Censored Variables","authors":"Guadalupe Gómez Melis, Ramon Oller, Klaus Langohr","doi":"10.1146/annurev-statistics-042424-103337","DOIUrl":"https://doi.org/10.1146/annurev-statistics-042424-103337","url":null,"abstract":"Survival analysis is essential for modeling time-to-event data across various fields, including medicine, engineering, and the social sciences. A major challenge in this field is handling censored data, particularly partly interval-censored data, where event times are either precisely recorded or only known to fall within a specific interval. Proper statistical modeling of such data is crucial for drawing valid conclusions and making accurate predictions. This article reviews regression models for analyzing interval-censored responses and their implementation in R. Following an introduction to the nonparametric maximum likelihood estimator, we focus on four major regression models: the accelerated failure time model, the proportional hazards model, the proportional odds model, and the generalized odds-rate model. For each, we review the state of the art, outline its methodology, discuss implementation strategies, and illustrate practical applications using real-world data. The article concludes with a discussion of current challenges, alternative modeling approaches, and potential directions for future research.","PeriodicalId":48855,"journal":{"name":"Annual Review of Statistics and Its Application","volume":"1 1","pages":""},"PeriodicalIF":7.9,"publicationDate":"2025-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145289381","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
Integrative Analysis of Multimodal Omics Data 多模态组学数据的综合分析
IF 7.9 1区 数学
Annual Review of Statistics and Its Application Pub Date : 2025-10-01 DOI: 10.1146/annurev-statistics-042424-113016
Gen Li, Eric F. Lock
{"title":"Integrative Analysis of Multimodal Omics Data","authors":"Gen Li, Eric F. Lock","doi":"10.1146/annurev-statistics-042424-113016","DOIUrl":"https://doi.org/10.1146/annurev-statistics-042424-113016","url":null,"abstract":"With advancements in technology and the decreasing cost of data acquisition, high-throughput omics data have become increasingly prevalent in biomedical research. These data are often collected across multiple omics modalities at different molecular levels, offering a comprehensive perspective on underlying biological mechanisms. However, the multimodal nature of multiomics data presents unique and complex challenges for statistical analysis. In this article, we provide a comprehensive review of recent advancements in statistical methods for multiomics data integration. We discuss key topics in unsupervised learning (including dimension reduction, clustering, and network analysis), supervised learning (including regression, classification, and mediation analysis), and other areas. Finally, we highlight unresolved challenges and propose promising directions for future research to further advance the field.","PeriodicalId":48855,"journal":{"name":"Annual Review of Statistics and Its Application","volume":"114 1","pages":""},"PeriodicalIF":7.9,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145203439","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
Change-Point Detection and Its Modern Applications 变点检测及其现代应用
IF 7.9 1区 数学
Annual Review of Statistics and Its Application Pub Date : 2025-09-10 DOI: 10.1146/annurev-statistics-041124-044143
Jialiang Li, Jingli Wang, Yuetao Yu
{"title":"Change-Point Detection and Its Modern Applications","authors":"Jialiang Li, Jingli Wang, Yuetao Yu","doi":"10.1146/annurev-statistics-041124-044143","DOIUrl":"https://doi.org/10.1146/annurev-statistics-041124-044143","url":null,"abstract":"We review recent advances in change-point detection methods across three important fields of statistics: (<jats:italic>a</jats:italic>) We first present a subgroup identification method based on a multi-threshold change plane model where the subgroup boundaries are defined by a high-dimensional hyperplane in the covariate space. Subjects grouped into different regions may receive more individualized treatments in medical research studies and achieve improved health outcomes. (<jats:italic>b</jats:italic>) We then consider the estimation of discontinuity for functional process data. Many longitudinal or functional responses may exhibit abrupt jumps, and our methodology effectively accommodates such complicated nonsmooth features. (<jats:italic>c</jats:italic>) Finally, we explore change-point estimation within dynamic networks using a recently proposed network autoregressive model. This framework demonstrates that community structures in networks can shift similarly to changes observed in time series data. These reviews highlight the wide-ranging applications of change-point detection methodologies in modern data analysis.","PeriodicalId":48855,"journal":{"name":"Annual Review of Statistics and Its Application","volume":"43 1","pages":""},"PeriodicalIF":7.9,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145043395","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
Disasters, Statistics, and the Humanitarian Sector 灾害、统计和人道主义部门
IF 7.9 1区 数学
Annual Review of Statistics and Its Application Pub Date : 2025-09-10 DOI: 10.1146/annurev-statistics-042424-061122
Hamish William Patten, Zineb Bhaby
{"title":"Disasters, Statistics, and the Humanitarian Sector","authors":"Hamish William Patten, Zineb Bhaby","doi":"10.1146/annurev-statistics-042424-061122","DOIUrl":"https://doi.org/10.1146/annurev-statistics-042424-061122","url":null,"abstract":"This article examines the role of statistics in the humanitarian sector, with a particular focus on disasters caused by natural hazards. It begins by outlining current applications, including primary data collection, anticipatory action frameworks, Earth observation, mobile positioning data, and artificial intelligence. It then highlights key challenges such as gaps and biases in disaster impact and response data, difficulties in communicating statistical findings clearly, inequities in aid allocation, and the widespread outsourcing of statistics-related work. In exploring future applications, the article discusses the potential of impact-based early warning models, dynamic population data, and artificial intelligence to enhance communication and decision-making. Throughout, emphasis is placed on the need for interoperable systems as well as ethical and inclusive data practices. In doing so, the article presents statistics as both a diagnostic and strategic tool for strengthening the effectiveness, fairness, and responsiveness of humanitarian action in disaster contexts.","PeriodicalId":48855,"journal":{"name":"Annual Review of Statistics and Its Application","volume":"85 1","pages":""},"PeriodicalIF":7.9,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145043391","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
The Enemies of Reliable and Useful Clinical Prediction Models: A Review of Statistical and Scientific Challenges 可靠和有用的临床预测模型的敌人：对统计和科学挑战的回顾
IF 7.9 1区 数学
Annual Review of Statistics and Its Application Pub Date : 2025-08-28 DOI: 10.1146/annurev-statistics-042324-123749
Ben Van Calster, Maarten van Smeden, Wouter van Amsterdam, Maarten Coemans, Laure Wynants, Ewout W. Steyerberg
{"title":"The Enemies of Reliable and Useful Clinical Prediction Models: A Review of Statistical and Scientific Challenges","authors":"Ben Van Calster, Maarten van Smeden, Wouter van Amsterdam, Maarten Coemans, Laure Wynants, Ewout W. Steyerberg","doi":"10.1146/annurev-statistics-042324-123749","DOIUrl":"https://doi.org/10.1146/annurev-statistics-042324-123749","url":null,"abstract":"The current status of applied clinical prediction modeling is poor. Many models are developed with suboptimal methods and are not evaluated, and hence have little impact on clinical care. We review 12 challenges—provocatively labeled enemies—that jeopardize the creation of prediction models that make it to clinical practice to improve treatment decisions and clinical outcomes for individual patients. The challenges cover four areas: context, data, design and analysis, and scientific culture. We provide negative examples and recommendations for improvement, but also highlight positive examples and developments. Greater awareness of the complexities surrounding clinical prediction modeling is needed among researchers, funding agencies, health professionals as end users, and all of us as potential patients. To improve the utility of prediction models for healthcare and society, we need fewer but better models as well as more resources for model validation, impact assessment, and implementation.","PeriodicalId":48855,"journal":{"name":"Annual Review of Statistics and Its Application","volume":"20 1","pages":""},"PeriodicalIF":7.9,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144915659","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
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