Pub Date : 2024-11-12DOI: 10.1146/annurev-statistics-112723-034225
Hsin-wen Chang, Ian W. McKeague
Functional data analysis (FDA) studies data that include infinite-dimensional functions or objects, generalizing traditional univariate or multivariate observations from each study unit. Among inferential approaches without parametric assumptions, empirical likelihood (EL) offers a principled method in that it extends the framework of parametric likelihood ratio–based inference via the nonparametric likelihood. There has been increasing use of EL in FDA due to its many favorable properties, including self-normalization and the data-driven shape of confidence regions. This article presents a review of EL approaches in FDA, starting with finite-dimensional features, then covering infinite-dimensional features. We contrast smooth and nonsmooth frameworks in FDA and show how EL has been incorporated into both of them. The article concludes with a discussion of some future research directions, including the possibility of applying EL to conformal inference.
功能数据分析（FDA）研究包括无限维函数或对象的数据，从每个研究单元概括传统的单变量或多变量观察结果。在没有参数假设的推论方法中，经验似然法（EL）提供了一种原则性方法，它通过非参数似然法扩展了基于参数似然比的推论框架。由于 EL 具有许多有利特性，包括自归一化和置信区的数据驱动形状，因此在 FDA 中的应用越来越多。本文综述了 EL 在 FDA 中的应用，从有限维特征开始，然后涵盖无限维特征。我们对比了 FDA 中的平滑框架和非平滑框架，并展示了 EL 如何融入这两种框架。文章最后讨论了一些未来的研究方向，包括将 EL 应用于保形推理的可能性。
{"title":"Empirical Likelihood in Functional Data Analysis","authors":"Hsin-wen Chang, Ian W. McKeague","doi":"10.1146/annurev-statistics-112723-034225","DOIUrl":"https://doi.org/10.1146/annurev-statistics-112723-034225","url":null,"abstract":"Functional data analysis (FDA) studies data that include infinite-dimensional functions or objects, generalizing traditional univariate or multivariate observations from each study unit. Among inferential approaches without parametric assumptions, empirical likelihood (EL) offers a principled method in that it extends the framework of parametric likelihood ratio–based inference via the nonparametric likelihood. There has been increasing use of EL in FDA due to its many favorable properties, including self-normalization and the data-driven shape of confidence regions. This article presents a review of EL approaches in FDA, starting with finite-dimensional features, then covering infinite-dimensional features. We contrast smooth and nonsmooth frameworks in FDA and show how EL has been incorporated into both of them. The article concludes with a discussion of some future research directions, including the possibility of applying EL to conformal inference.","PeriodicalId":48855,"journal":{"name":"Annual Review of Statistics and Its Application","volume":"60 1","pages":""},"PeriodicalIF":7.9,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142601275","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}
Pub Date : 2024-10-30DOI: 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
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.
因果中介分析为整合不同类型的暴露、基因组和表型数据提供了一个极具吸引力的框架。最近，人们对这一领域的兴趣大增，主要原因是健康和社会科学领域对因果中介分析的需求日益增长。本文旨在综述中介分析的最新进展，包括对单一中介和大量中介的中介分析，以及对多重暴露和中介的中介分析。我们的综述侧重于因果中介分析统计推断的最新进展，尤其是在高维中介分析方面。我们深入探讨了检验中介效应的复杂性，特别是解决检验大量复合零假设的难题。通过大量的模拟研究，我们比较了各种情况下的现有方法。我们还分析了 "正常老龄化研究"（Normative Aging Study）的数据，该研究将 DNA 甲基化 CpG 位点作为吸烟状态对肺功能影响的潜在中介。我们将讨论这些方法的优缺点以及未来的研究方向。
{"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}
Pub Date : 2024-10-30DOI: 10.1146/annurev-statistics-033121-120121
M. Elizabeth Halloran
Due to dependent happenings, vaccines can have different effects in populations. In addition to direct protective effects in the vaccinated, vaccination in a population can have indirect effects in the unvaccinated individuals. Vaccination can also reduce person-to-person transmission to vaccinated individuals or from vaccinated individuals compared with unvaccinated individuals. Design of vaccine studies has a history extending back over a century. Emerging infectious diseases, such as the SARS-CoV-2 pandemic and the Ebola outbreak in West Africa, have stimulated new interest in vaccine studies. We focus on some recent developments, such as target trial emulation, test-negative design, and regression discontinuity design. Methods for evaluating durability of vaccine effects were developed in the context of both blinded and unblinded placebo crossover studies. The case-ascertained design is used to assess the transmission effects of vaccines. The novel ring vaccination trial design was first used in the Ebola outbreak in West Africa.
{"title":"Designs for Vaccine Studies","authors":"M. Elizabeth Halloran","doi":"10.1146/annurev-statistics-033121-120121","DOIUrl":"https://doi.org/10.1146/annurev-statistics-033121-120121","url":null,"abstract":"Due to dependent happenings, vaccines can have different effects in populations. In addition to direct protective effects in the vaccinated, vaccination in a population can have indirect effects in the unvaccinated individuals. Vaccination can also reduce person-to-person transmission to vaccinated individuals or from vaccinated individuals compared with unvaccinated individuals. Design of vaccine studies has a history extending back over a century. Emerging infectious diseases, such as the SARS-CoV-2 pandemic and the Ebola outbreak in West Africa, have stimulated new interest in vaccine studies. We focus on some recent developments, such as target trial emulation, test-negative design, and regression discontinuity design. Methods for evaluating durability of vaccine effects were developed in the context of both blinded and unblinded placebo crossover studies. The case-ascertained design is used to assess the transmission effects of vaccines. The novel ring vaccination trial design was first used in the Ebola outbreak in West Africa.","PeriodicalId":48855,"journal":{"name":"Annual Review of Statistics and Its Application","volume":"5 1","pages":""},"PeriodicalIF":7.9,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142555733","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}
Pub Date : 2024-10-18DOI: 10.1146/annurev-statistics-112723-034158
Weijie J. Su
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.
差分隐私因其稳健而严格的保证，被广泛认为是隐私保护数据分析的正式隐私，在公共服务、学术界和工业界得到越来越广泛的应用。虽然差分隐私起源于密码学，但在本综述中，我们认为从根本上讲，它可以被视为一个纯粹的统计学概念。我们利用布莱克韦尔（Blackwell）的信息性定理，重点论证了差分隐私的定义可以从假设检验的角度正式提出，从而表明假设检验不仅方便，而且是推理差分隐私的正确语言。这一见解引出了 f 差分隐私的定义，它通过表示定理扩展了其他差分隐私定义。我们回顾了一些技术，这些技术使 f 差分隐私成为分析数据分析和机器学习中隐私边界的统一框架。我们讨论了这种差分隐私定义在私有深度学习、私有凸优化、洗牌机制和美国人口普查数据中的应用，以突出与现有替代方法相比，在此框架下分析隐私边界的优势。
{"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}
Pub Date : 2024-10-09DOI: 10.1146/annurev-statistics-112723-034436
Mine Dogucu
Difficulties in reproducing results from scientific studies have lately been referred to as a reproducibility crisis. Scientific practice depends heavily on scientific training. What gets taught in the classroom is often practiced in labs, fields, and data analysis. The importance of reproducibility in the classroom has gained momentum in statistics education in recent years. In this article, we review the existing literature on reproducibility education. We delve into the relationship between computing tools and reproducibility through visiting historical developments in this area. We share examples for teaching reproducibility and reproducible teaching while discussing the pedagogical opportunities created by these examples as well as challenges that the instructors should be aware of. We detail the use of teaching reproducibility and reproducible teaching practices in an introductory data science course. Lastly, we provide recommendations on reproducibility education for instructors, administrators, and other members of the scientific community.
{"title":"Reproducibility in the Classroom","authors":"Mine Dogucu","doi":"10.1146/annurev-statistics-112723-034436","DOIUrl":"https://doi.org/10.1146/annurev-statistics-112723-034436","url":null,"abstract":"Difficulties in reproducing results from scientific studies have lately been referred to as a reproducibility crisis. Scientific practice depends heavily on scientific training. What gets taught in the classroom is often practiced in labs, fields, and data analysis. The importance of reproducibility in the classroom has gained momentum in statistics education in recent years. In this article, we review the existing literature on reproducibility education. We delve into the relationship between computing tools and reproducibility through visiting historical developments in this area. We share examples for teaching reproducibility and reproducible teaching while discussing the pedagogical opportunities created by these examples as well as challenges that the instructors should be aware of. We detail the use of teaching reproducibility and reproducible teaching practices in an introductory data science course. Lastly, we provide recommendations on reproducibility education for instructors, administrators, and other members of the scientific community.","PeriodicalId":48855,"journal":{"name":"Annual Review of Statistics and Its Application","volume":"2 1","pages":""},"PeriodicalIF":7.9,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142398006","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}
Pub Date : 2024-10-07DOI: 10.1146/annurev-statistics-112723-034249
Simon N. Wood
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.
{"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}
Pub Date : 2024-10-01DOI: 10.1146/annurev-statistics-112723-034642
Shahin Tavakoli, Beatrice Matteo, Davide Pigoli, Eleanor Chodroff, John Coleman, Michele Gubian, Margaret E.L. Renwick, Morgan Sonderegger
Phonetics is the scientific field concerned with the study of how speech is produced, heard, and perceived. It abounds with data, such as acoustic speech recordings, neuroimaging data, or articulatory data. In this article, we provide an introduction to different areas of phonetics (acoustic phonetics, sociophonetics, speech perception, articulatory phonetics, speech inversion, sound change, and speech technology), an overview of the statistical methods for analyzing their data, and an introduction to the signal processing methods commonly applied to speech recordings. A major transition in the statistical modeling of phonetic data has been the shift from fixed effects to random effects regression models, the modeling of curve data (for instance, via generalized additive mixed models or functional data analysis methods), and the use of Bayesian methods. This shift has been driven in part by the increased focus on large speech corpora in phonetics, which has arisen from machine learning methods such as forced alignment. We conclude by identifying opportunities for future research.
{"title":"Statistics in Phonetics","authors":"Shahin Tavakoli, Beatrice Matteo, Davide Pigoli, Eleanor Chodroff, John Coleman, Michele Gubian, Margaret E.L. Renwick, Morgan Sonderegger","doi":"10.1146/annurev-statistics-112723-034642","DOIUrl":"https://doi.org/10.1146/annurev-statistics-112723-034642","url":null,"abstract":"Phonetics is the scientific field concerned with the study of how speech is produced, heard, and perceived. It abounds with data, such as acoustic speech recordings, neuroimaging data, or articulatory data. In this article, we provide an introduction to different areas of phonetics (acoustic phonetics, sociophonetics, speech perception, articulatory phonetics, speech inversion, sound change, and speech technology), an overview of the statistical methods for analyzing their data, and an introduction to the signal processing methods commonly applied to speech recordings. A major transition in the statistical modeling of phonetic data has been the shift from fixed effects to random effects regression models, the modeling of curve data (for instance, via generalized additive mixed models or functional data analysis methods), and the use of Bayesian methods. This shift has been driven in part by the increased focus on large speech corpora in phonetics, which has arisen from machine learning methods such as forced alignment. We conclude by identifying opportunities for future research.","PeriodicalId":48855,"journal":{"name":"Annual Review of Statistics and Its Application","volume":"32 1","pages":""},"PeriodicalIF":7.9,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142363010","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}
Pub Date : 2024-10-01DOI: 10.1146/annurev-statistics-112723-034304
Patrick J. Laub, Young Lee, Philip K. Pollett, Thomas Taimre
The Hawkes process is a model for counting the number of arrivals to a system that exhibits the self-exciting property—that one arrival creates a heightened chance of further arrivals in the near future. The model and its generalizations have been applied in a plethora of disparate domains, though two particularly developed applications are in seismology and in finance. As the original model is elegantly simple, generalizations have been proposed that track marks for each arrival, are multivariate, have a spatial component, are driven by renewal processes, treat time as discrete, and so on. This article creates a cohesive review of the traditional Hawkes model and the modern generalizations, providing details on their construction and simulation algorithms, and giving key references to the appropriate literature for a detailed treatment.
{"title":"Hawkes Models and Their Applications","authors":"Patrick J. Laub, Young Lee, Philip K. Pollett, Thomas Taimre","doi":"10.1146/annurev-statistics-112723-034304","DOIUrl":"https://doi.org/10.1146/annurev-statistics-112723-034304","url":null,"abstract":"The Hawkes process is a model for counting the number of arrivals to a system that exhibits the self-exciting property—that one arrival creates a heightened chance of further arrivals in the near future. The model and its generalizations have been applied in a plethora of disparate domains, though two particularly developed applications are in seismology and in finance. As the original model is elegantly simple, generalizations have been proposed that track marks for each arrival, are multivariate, have a spatial component, are driven by renewal processes, treat time as discrete, and so on. This article creates a cohesive review of the traditional Hawkes model and the modern generalizations, providing details on their construction and simulation algorithms, and giving key references to the appropriate literature for a detailed treatment.","PeriodicalId":48855,"journal":{"name":"Annual Review of Statistics and Its Application","volume":"58 1","pages":""},"PeriodicalIF":7.9,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142363011","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}
Pub Date : 2024-09-26DOI: 10.1146/annurev-statistics-112723-034721
Hyunseung Kang, Zijian Guo, Zhonghua Liu, Dylan Small
Instrumental variables (IVs) are widely used to study the causal effect of an exposure on an outcome in the presence of unmeasured confounding. IVs require an instrument, a variable that (a) is associated with the exposure, (b) has no direct effect on the outcome except through the exposure, and (c) is not related to unmeasured confounders. Unfortunately, finding variables that satisfy conditions b or c can be challenging in practice. This article reviews works where instruments may not satisfy conditions b or c, which we refer to as invalid instruments. We review identification and inference under different violations of b or c, specifically under linear models, nonlinear models, and heteroskedastic models. We conclude with an empirical comparison of various methods by reanalyzing the effect of body mass index on systolic blood pressure from the UK Biobank.
工具变量（IVs）被广泛用于研究在存在未测量混杂因素的情况下暴露对结果的因果效应。工具变量需要一个工具，一个（a）与暴露相关的变量，（b）除了通过暴露对结果没有直接影响的变量，以及（c）与未测量混杂因素无关的变量。遗憾的是，要找到满足条件 b 或 c 的变量在实践中可能很困难。本文回顾了工具可能不满足条件 b 或 c 的研究，我们称之为无效工具。我们回顾了不同的 b 或 c 条件下的识别和推断，特别是线性模型、非线性模型和异方差模型。最后，我们通过重新分析英国生物库中体重指数对收缩压的影响，对各种方法进行了实证比较。
{"title":"Identification and Inference with Invalid Instruments","authors":"Hyunseung Kang, Zijian Guo, Zhonghua Liu, Dylan Small","doi":"10.1146/annurev-statistics-112723-034721","DOIUrl":"https://doi.org/10.1146/annurev-statistics-112723-034721","url":null,"abstract":"Instrumental variables (IVs) are widely used to study the causal effect of an exposure on an outcome in the presence of unmeasured confounding. IVs require an instrument, a variable that (a) is associated with the exposure, (b) has no direct effect on the outcome except through the exposure, and (c) is not related to unmeasured confounders. Unfortunately, finding variables that satisfy conditions b or c can be challenging in practice. This article reviews works where instruments may not satisfy conditions b or c, which we refer to as invalid instruments. We review identification and inference under different violations of b or c, specifically under linear models, nonlinear models, and heteroskedastic models. We conclude with an empirical comparison of various methods by reanalyzing the effect of body mass index on systolic blood pressure from the UK Biobank.","PeriodicalId":48855,"journal":{"name":"Annual Review of Statistics and Its Application","volume":"735 1","pages":""},"PeriodicalIF":7.9,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142321065","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}
Pub Date : 2024-09-11DOI: 10.1146/annurev-statistics-040522-100329
Martin A. Lindquist, Bonnie B. Smith, Arunkumar Kannan, Angela Zhao, Brian Caffo
The emergence of functional magnetic resonance imaging (fMRI) marked a significant technological breakthrough in the real-time measurement of the functioning human brain in vivo. In part because of their 4D nature (three spatial dimensions and time), fMRI data have inspired a great deal of statistical development in the past couple of decades to address their unique spatiotemporal properties. This article provides an overview of the current landscape in functional brain measurement, with a particular focus on fMRI, highlighting key developments in the past decade. Furthermore, it looks ahead to the future, discussing unresolved research questions in the community and outlining potential research topics for the future.
{"title":"Measuring the Functioning Human Brain","authors":"Martin A. Lindquist, Bonnie B. Smith, Arunkumar Kannan, Angela Zhao, Brian Caffo","doi":"10.1146/annurev-statistics-040522-100329","DOIUrl":"https://doi.org/10.1146/annurev-statistics-040522-100329","url":null,"abstract":"The emergence of functional magnetic resonance imaging (fMRI) marked a significant technological breakthrough in the real-time measurement of the functioning human brain in vivo. In part because of their 4D nature (three spatial dimensions and time), fMRI data have inspired a great deal of statistical development in the past couple of decades to address their unique spatiotemporal properties. This article provides an overview of the current landscape in functional brain measurement, with a particular focus on fMRI, highlighting key developments in the past decade. Furthermore, it looks ahead to the future, discussing unresolved research questions in the community and outlining potential research topics for the future.","PeriodicalId":48855,"journal":{"name":"Annual Review of Statistics and Its Application","volume":"50 1","pages":""},"PeriodicalIF":7.9,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142170864","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}
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