Annual Review of Statistics and Its Application最新文献_第2页

Statistical Methods in Aging Research: Improving Current Practices and Embracing Emerging Approaches 老龄化研究中的统计方法：改进当前的实践和拥抱新兴的方法

IF 7.9 1区数学

Annual Review of Statistics and Its Application Pub Date : 2025-11-18 DOI: 10.1146/annurev-statistics-042324-060005

Deependra K. Thapa, Erik S. Parker, Mounika Kandukuri, Xi (Rita) Wang, Thirupathi R. Mokalla, Olivia C. Robertson, Wasiuddin Najam, Andrew E. Teschendorff, Andrew W. Brown, John R. Speakman, Yisheng Peng, Bernard S. Gorman, Heping Zhang, Luis-Enrique Becerra-Garcia, Colby J. Vorland, David B. Allison

{"title":"Statistical Methods in Aging Research: Improving Current Practices and Embracing Emerging Approaches","authors":"Deependra K. Thapa, Erik S. Parker, Mounika Kandukuri, Xi (Rita) Wang, Thirupathi R. Mokalla, Olivia C. Robertson, Wasiuddin Najam, Andrew E. Teschendorff, Andrew W. Brown, John R. Speakman, Yisheng Peng, Bernard S. Gorman, Heping Zhang, Luis-Enrique Becerra-Garcia, Colby J. Vorland, David B. Allison","doi":"10.1146/annurev-statistics-042324-060005","DOIUrl":"https://doi.org/10.1146/annurev-statistics-042324-060005","url":null,"abstract":"Aging research relies on varied statistical methods, and applying these methods appropriately is important for scientific rigor. However, proper use of these statistical techniques is a challenge. We discuss two categories of statistical methods in aging research: ( a ) emerging methods requiring further validation, including techniques to examine compression of morbidity, maximum lifespan, immortal time bias, molecular aging clocks, and treatment response heterogeneity, and ( b ) classic and existing methods needing reconsideration and improvement, such as stepwise regression, generalized linear models, methods for accounting for clustering and nesting effects, methods for testing for group differences, methods for mediation and moderation analyses, and nonlinear models. For each method, we review its relevance to aging research, highlight statistical issues, and suggest improvements or alternatives with examples from aging research. We urge researchers to refine traditional approaches and embrace emerging methods tailored to the unique challenges of aging research. This review will help researchers identify and apply sound statistical methods, thereby improving statistical rigor in aging research.","PeriodicalId":48855,"journal":{"name":"Annual Review of Statistics and Its Application","volume":"101 1","pages":""},"PeriodicalIF":7.9,"publicationDate":"2025-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145545454","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 Learning for Functional Data 功能数据的统计学习

IF 7.9 1区数学

Annual Review of Statistics and Its Application Pub Date : 2025-11-17 DOI: 10.1146/annurev-statistics-042424-052503

Jiguo Cao, Sidi Wu, Muye Nanshan, Haolun Shi, Liangliang Wang

{"title":"Statistical Learning for Functional Data","authors":"Jiguo Cao, Sidi Wu, Muye Nanshan, Haolun Shi, Liangliang Wang","doi":"10.1146/annurev-statistics-042424-052503","DOIUrl":"https://doi.org/10.1146/annurev-statistics-042424-052503","url":null,"abstract":"Functional data analysis (FDA) is a rapidly growing field in modern statistics that provides powerful tools for analyzing data observed as curves, surfaces, or more general functions. Unlike traditional multivariate methods, FDA explicitly accounts for the smooth and continuous nature of functional data, enabling more accurate modeling and interpretation. Traditional FDA methods, such as functional principal component analysis, functional regression, and functional classification, rely on linear assumptions and basis function expansions, which can limit their effectiveness when applied to nonlinear, high-dimensional, or irregularly sampled data. Recent advances in neural networks provide promising alternatives to these traditional approaches. Deep learning methods offer several key advantages: They naturally capture nonlinear relationships, scale to high-dimensional data without explicit dimension reduction, learn task-specific representations directly from raw observations, and handle sparse or irregular sampling without requiring imputation. This article reviews recent methodological developments in FDA, with a focus on the integration of deep learning techniques. Through this comparative review, we highlight the strengths and limitations of classical and modern approaches, providing practical guidance and future directions.","PeriodicalId":48855,"journal":{"name":"Annual Review of Statistics and Its Application","volume":"155 1","pages":""},"PeriodicalIF":7.9,"publicationDate":"2025-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145536105","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

Proper Scoring Rules for Estimation and Forecast Evaluation 评估和预测评估的正确评分规则

IF 7.9 1区数学

Annual Review of Statistics and Its Application Pub Date : 2025-11-14 DOI: 10.1146/annurev-statistics-042424-050626

Kartik Waghmare, Johanna Ziegel

引用次数: 0

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

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

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

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

引用次数: 0