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

Neural Methods for Amortized Inference 用于摊销推理的神经方法

IF 7.9 1区数学

Annual Review of Statistics and Its Application Pub Date : 2024-11-12 DOI: 10.1146/annurev-statistics-112723-034123

Andrew Zammit-Mangion, Matthew Sainsbury-Dale, Raphaël Huser

引用次数: 0

Infectious Disease Modeling 传染病建模

IF 7.9 1区数学

Annual Review of Statistics and Its Application Pub Date : 2024-11-12 DOI: 10.1146/annurev-statistics-112723-034351

Jing Huang, Jeffrey S. Morris

{"title":"Infectious Disease Modeling","authors":"Jing Huang, Jeffrey S. Morris","doi":"10.1146/annurev-statistics-112723-034351","DOIUrl":"https://doi.org/10.1146/annurev-statistics-112723-034351","url":null,"abstract":"Infectious diseases pose a persistent challenge to public health worldwide. Recent global health crises, such as the COVID-19 pandemic and Ebola outbreaks, have underscored the vital role of infectious disease modeling in guiding public health policy and response. Infectious disease modeling is a critical tool for society, informing risk mitigation measures, prompting timely interventions, and aiding preparedness for healthcare delivery systems. This article synthesizes the current landscape of infectious disease modeling, emphasizing the integration of statistical methods in understanding and predicting the spread of infectious diseases. We begin by examining the historical context and the foundational models that have shaped the field, such as the SIR (susceptible, infectious, recovered) and SEIR (susceptible, exposed, infectious, recovered) models. Subsequently, we delve into the methodological innovations that have arisen, including stochastic modeling, network-based approaches, and the use of big data analytics. We also explore the integration of machine learning techniques in enhancing model accuracy and responsiveness. The review identifies the challenges of parameter estimation, model validation, and the incorporation of real-time data streams. Moreover, we discuss the ethical implications of modeling, such as privacy concerns and the communication of risk. The article concludes by discussing future directions for research, highlighting the need for data integration and interdisciplinary collaboration for advancing infectious disease modeling.","PeriodicalId":48855,"journal":{"name":"Annual Review of Statistics and Its Application","volume":"15 1","pages":""},"PeriodicalIF":7.9,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142601274","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

Tensors in High-Dimensional Data Analysis: Methodological Opportunities and Theoretical Challenges 高维数据分析中的张量：方法论机遇与理论挑战

IF 7.9 1区数学

Annual Review of Statistics and Its Application Pub Date : 2024-11-12 DOI: 10.1146/annurev-statistics-112723-034548

Arnab Auddy, Dong Xia, Ming Yuan

引用次数: 0

Excess Mortality Estimation 超额死亡率估算

IF 7.9 1区数学

Annual Review of Statistics and Its Application Pub Date : 2024-11-12 DOI: 10.1146/annurev-statistics-112723-034236

Jon Wakefield, Victoria Knutson

{"title":"Excess Mortality Estimation","authors":"Jon Wakefield, Victoria Knutson","doi":"10.1146/annurev-statistics-112723-034236","DOIUrl":"https://doi.org/10.1146/annurev-statistics-112723-034236","url":null,"abstract":"Estimating the mortality associated with a specific mortality crisis event (for example, a pandemic, natural disaster, or conflict) is clearly an important public health undertaking. In many situations, deaths may be directly or indirectly attributable to the mortality crisis event, and both contributions may be of interest. The totality of the mortality impact on the population (direct and indirect deaths) includes the knock-on effects of the event, such as a breakdown of the health care system, or increased mortality due to shortages of resources. Unfortunately, estimating the deaths directly attributable to the event is frequently problematic. Hence, the excess mortality, defined as the difference between the observed mortality and that which would have occurred in the absence of the crisis event, is an estimation target. If the region of interest contains a functioning vital registration system, so that the mortality is fully observed and reliable, then the only modeling required is to produce the expected deaths counts, but this is a nontrivial exercise. In low- and middle-income countries it is common for there to be incomplete (or nonexistent) mortality data, and one must then use additional data and/or modeling, including predicting mortality using auxiliary variables. We describe and review each of these aspects, give examples of excess mortality studies, and provide a case study on excess mortality across states of the United States during the COVID-19 pandemic.","PeriodicalId":48855,"journal":{"name":"Annual Review of Statistics and Its Application","volume":"6 1","pages":""},"PeriodicalIF":7.9,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142601402","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

Empirical Likelihood in Functional Data Analysis 函数数据分析中的经验似然法

IF 7.9 1区数学

Annual Review of Statistics and Its Application Pub Date : 2024-11-12 DOI: 10.1146/annurev-statistics-112723-034225

Hsin-wen Chang, Ian W. McKeague

引用次数: 0

Causal Mediation Analysis for Integrating Exposure, Genomic, and Phenotype Data 整合暴露、基因组和表型数据的因果中介分析

IF 7.9 1区数学

Annual Review of Statistics and Its Application Pub Date : 2024-10-30 DOI: 10.1146/annurev-statistics-040622-031653

Haoyu Yang, Zhonghua Liu, Ruoyu Wang, En-Yu Lai, Joel Schwartz, Andrea A. Baccarelli, Yen-Tsung Huang, Xihong Lin

{"title":"Causal Mediation Analysis for Integrating Exposure, Genomic, and Phenotype Data","authors":"Haoyu Yang, Zhonghua Liu, Ruoyu Wang, En-Yu Lai, Joel Schwartz, Andrea A. Baccarelli, Yen-Tsung Huang, Xihong Lin","doi":"10.1146/annurev-statistics-040622-031653","DOIUrl":"https://doi.org/10.1146/annurev-statistics-040622-031653","url":null,"abstract":"Causal mediation analysis provides an attractive framework for integrating diverse types of exposure, genomic, and phenotype data. Recently, this field has seen a surge of interest, largely driven by the increasing need for causal mediation analyses in health and social sciences. This article aims to provide a review of recent developments in mediation analysis, encompassing mediation analysis of a single mediator and a large number of mediators, as well as mediation analysis with multiple exposures and mediators. Our review focuses on the recent advancements in statistical inference for causal mediation analysis, especially in the context of high-dimensional mediation analysis. We delve into the complexities of testing mediation effects, especially addressing the challenge of testing a large number of composite null hypotheses. Through extensive simulation studies, we compare the existing methods across a range of scenarios. We also include an analysis of data from the Normative Aging Study, which examines DNA methylation CpG sites as potential mediators of the effect of smoking status on lung function. We discuss the pros and cons of these methods and future research directions.","PeriodicalId":48855,"journal":{"name":"Annual Review of Statistics and Its Application","volume":"26 1","pages":""},"PeriodicalIF":7.9,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142555732","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

Designs for Vaccine Studies 疫苗研究设计

IF 7.9 1区数学

Annual Review of Statistics and Its Application Pub Date : 2024-10-30 DOI: 10.1146/annurev-statistics-033121-120121

M. Elizabeth Halloran

引用次数: 0

A Statistical Viewpoint on Differential Privacy: Hypothesis Testing, Representation, and Blackwell's Theorem 关于差异隐私的统计学观点：假设检验、表征和布莱克韦尔定理

IF 7.9 1区数学

Annual Review of Statistics and Its Application Pub Date : 2024-10-18 DOI: 10.1146/annurev-statistics-112723-034158

Weijie J. Su

{"title":"A Statistical Viewpoint on Differential Privacy: Hypothesis Testing, Representation, and Blackwell's Theorem","authors":"Weijie J. Su","doi":"10.1146/annurev-statistics-112723-034158","DOIUrl":"https://doi.org/10.1146/annurev-statistics-112723-034158","url":null,"abstract":"Differential privacy is widely considered the formal privacy for privacy-preserving data analysis due to its robust and rigorous guarantees, with increasingly broad adoption in public services, academia, and industry. Although differential privacy originated in the cryptographic context, in this review we argue that, fundamentally, it can be considered a pure statistical concept. We leverage Blackwell's informativeness theorem and focus on demonstrating that the definition of differential privacy can be formally motivated from a hypothesis testing perspective, thereby showing that hypothesis testing is not merely convenient but also the right language for reasoning about differential privacy. This insight leads to the definition of f-differential privacy, which extends other differential privacy definitions through a representation theorem. We review techniques that render f-differential privacy a unified framework for analyzing privacy bounds in data analysis and machine learning. Applications of this differential privacy definition to private deep learning, private convex optimization, shuffled mechanisms, and US Census data are discussed to highlight the benefits of analyzing privacy bounds under this framework compared with existing alternatives.","PeriodicalId":48855,"journal":{"name":"Annual Review of Statistics and Its Application","volume":"55 1","pages":""},"PeriodicalIF":7.9,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142449536","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

Reproducibility in the Classroom 课堂上的可重复性

IF 7.9 1区数学

Annual Review of Statistics and Its Application Pub Date : 2024-10-09 DOI: 10.1146/annurev-statistics-112723-034436

Mine Dogucu

引用次数: 0

Generalized Additive Models 广义加法模型

IF 7.9 1区数学

Annual Review of Statistics and Its Application Pub Date : 2024-10-07 DOI: 10.1146/annurev-statistics-112723-034249

Simon N. Wood

{"title":"Generalized Additive Models","authors":"Simon N. Wood","doi":"10.1146/annurev-statistics-112723-034249","DOIUrl":"https://doi.org/10.1146/annurev-statistics-112723-034249","url":null,"abstract":"Generalized additive models are generalized linear models in which the linear predictor includes a sum of smooth functions of covariates, where the shape of the functions is to be estimated. They have also been generalized beyond the original generalized linear model setting to distributions outside the exponential family and to situations in which multiple parameters of the response distribution may depend on sums of smooth functions of covariates. The widely used computational and inferential framework in which the smooth terms are represented as latent Gaussian processes, splines, or Gaussian random effects is reviewed, paying particular attention to the case in which computational and theoretical tractability is obtained by prior rank reduction of the model terms. An empirical Bayes approach is taken, and its relatively good frequentist performance discussed, along with some more overtly frequentist approaches to model selection. Estimation of the degree of smoothness of component functions via cross validation or marginal likelihood is covered, alongside the computational strategies required in practice, including when data and models are reasonably large. It is briefly shown how the framework extends easily to location-scale modeling, and, with more effort, to techniques such as quantile regression. Also covered are the main classes of smooths of multiple covariates that may be included in models: isotropic splines and tensor product smooth interaction terms.","PeriodicalId":48855,"journal":{"name":"Annual Review of Statistics and Its Application","volume":"39 1","pages":""},"PeriodicalIF":7.9,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142384162","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