Statistical Science最新文献_第7页

Seven Principles for Rapid-Response Data Science: Lessons Learned from Covid-19 Forecasting 快速响应数据科学的七项原则：从新冠肺炎预测中吸取的经验教训

IF 5.7 1区数学

Statistical Science Pub Date : 2021-08-19 DOI: 10.1214/22-sts855

Bin Yu, Chandan Singh

引用次数: 3

Random Matrix Theory and Its Applications 随机矩阵理论及其应用

IF 5.7 1区数学

Statistical Science Pub Date : 2021-08-01 DOI: 10.1142/9789814273121

A. Izenman

引用次数: 2

Khinchin’s 1929 Paper on Von Mises’ Frequency Theory of Probability 钦钦1929年关于冯·米塞斯概率频率理论的论文

IF 5.7 1区数学

Statistical Science Pub Date : 2021-08-01 DOI: 10.1214/20-sts798

L. Verburgt

引用次数: 0

Statistical Modeling for Practical Pooled Testing During the COVID-19 Pandemic 新冠肺炎大流行期间实际汇集测试的统计模型

IF 5.7 1区数学

Statistical Science Pub Date : 2021-07-12 DOI: 10.1214/22-sts857

S. Comess, H. Wang, S. Holmes, Claire Donnat

{"title":"Statistical Modeling for Practical Pooled Testing During the COVID-19 Pandemic","authors":"S. Comess, H. Wang, S. Holmes, Claire Donnat","doi":"10.1214/22-sts857","DOIUrl":"https://doi.org/10.1214/22-sts857","url":null,"abstract":"Pooled testing offers an efficient solution to the unprecedented testing demands of the COVID-19 pandemic, although with potentially lower sensitivity and increased costs to implementation in some settings. Assessments of this trade-off typically assume pooled specimens are independent and identically distributed. Yet, in the context of COVID-19, these assumptions are often violated: testing done on networks (housemates, spouses, co-workers) captures correlated individuals, while infection risk varies substantially across time, place and individuals. Neglecting dependencies and heterogeneity may bias established optimality grids and induce a sub-optimal implementation of the procedure. As a lesson learned from this pandemic, this paper highlights the necessity of integrating field sampling information with statistical modeling to efficiently optimize pooled testing. Using real data, we show that (a) greater gains can be achieved at low logistical cost by exploiting natural correlations (non-independence) between samples -- allowing improvements in sensitivity and efficiency of up to 30% and 90% respectively;and (b) these gains are robust despite substantial heterogeneity across pools (non-identical). Our modeling results complement and extend the observations of Barak et al (2021) who report an empirical sensitivity well beyond expectations. Finally, we provide an interactive tool for selecting an optimal pool size using contextual information","PeriodicalId":51172,"journal":{"name":"Statistical Science","volume":" ","pages":""},"PeriodicalIF":5.7,"publicationDate":"2021-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46125680","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: 8

Randomization-Based Test for Censored Outcomes: A New Look at the Logrank Test 基于随机化的检查结果检验：Logrank检验的新视角

IF 5.7 1区数学

Statistical Science Pub Date : 2021-07-06 DOI: 10.1214/22-sts851

Xinran Li, Dylan S. Small

{"title":"Randomization-Based Test for Censored Outcomes: A New Look at the Logrank Test","authors":"Xinran Li, Dylan S. Small","doi":"10.1214/22-sts851","DOIUrl":"https://doi.org/10.1214/22-sts851","url":null,"abstract":"Two-sample tests with censored outcomes are a classical topic in statistics with wide use even in cutting edge applications. There are at least two modes of inference used to justify two-sample tests. One is usual superpopulation inference assuming that units are independent and identically distributed (i.i.d.) samples from some superpopulation; the other is finite population inference that relies on the random assignments of units into different groups. When randomization is actually implemented, the latter has the advantage of avoiding distributional assumptions on the outcomes. In this paper, we focus on finite population inference for censored outcomes, which has been less explored in the literature. Moreover, we allow the censoring time to depend on treatment assignment, under which exact permutation inference is unachievable. We find that, surprisingly, the usual logrank test can also be justified by randomization. Specifically, under a Bernoulli randomized experiment with non-informative i.i.d. censoring, the logrank test is asymptotically valid for testing Fisher’s null hypothesis of no treatment effect on any unit. The asymptotic validity of the logrank test does not require any distributional assumption on the potential event times. We further extend the theory to the stratified logrank test, which is useful for randomized block designs and when censoring mechanisms vary across strata. In sum, the developed theory for the logrank test from finite population inference supplements its classical theory from usual superpopulation inference, and helps provide a broader justification for the logrank test.","PeriodicalId":51172,"journal":{"name":"Statistical Science","volume":" ","pages":""},"PeriodicalIF":5.7,"publicationDate":"2021-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47475631","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: 1

Stein’s Method Meets Computational Statistics: A Review of Some Recent Developments 斯坦因的方法与计算统计:一些最新发展的回顾

IF 5.7 1区数学

Statistical Science Pub Date : 2021-05-07 DOI: 10.1214/22-sts863

Andreas Anastasiou, A. Barp, F. Briol, B. Ebner, Robert E. Gaunt, Fatemeh Ghaderinezhad, Jackson Gorham, A. Gretton, Christophe Ley, Qiang Liu, Lester W. Mackey, C. Oates, G. Reinert, Yvik Swan

引用次数: 17

The Costs and Benefits of Uniformly Valid Causal Inference with High-Dimensional Nuisance Parameters 具有高维妨害参数的一致有效因果推理的成本与收益

IF 5.7 1区数学

Statistical Science Pub Date : 2021-05-05 DOI: 10.1214/21-sts843

Niloofar Moosavi, J. Haggstrom, X. Luna

{"title":"The Costs and Benefits of Uniformly Valid Causal Inference with High-Dimensional Nuisance Parameters","authors":"Niloofar Moosavi, J. Haggstrom, X. Luna","doi":"10.1214/21-sts843","DOIUrl":"https://doi.org/10.1214/21-sts843","url":null,"abstract":"Important advances have recently been achieved in developing procedures yielding uniformly valid inference for a low dimensional causal parameter when high-dimensional nuisance models must be estimated. In this paper, we review the literature on uniformly valid causal inference and discuss the costs and benefits of using uniformly valid inference procedures. Naive estimation strategies based on regularisation, machine learning, or a preliminary model selection stage for the nuisance models have finite sample distributions which are badly approximated by their asymptotic distributions. To solve this serious problem, estimators which converge uniformly in distribution over a class of data generating mechanisms have been proposed in the literature. In order to obtain uniformly valid results in high-dimensional situations, sparsity conditions for the nuisance models need typically to be made, although a double robustness property holds, whereby if one of the nuisance model is more sparse, the other nuisance model is allowed to be less sparse. While uniformly valid inference is a highly desirable property, uniformly valid procedures pay a high price in terms of inflated variability. Our discussion of this dilemma is illustrated by the study of a double-selection outcome regression estimator, which we show is uniformly asymptotically unbiased, but is less variable than uniformly valid estimators in the numerical experiments conducted.","PeriodicalId":51172,"journal":{"name":"Statistical Science","volume":" ","pages":""},"PeriodicalIF":5.7,"publicationDate":"2021-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48898490","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: 7

Comment: Settle the Unsettling: An Inferential Models Perspective 评论：解决不安：一个推理模型的视角

IF 5.7 1区数学

Statistical Science Pub Date : 2021-05-01 DOI: 10.1214/21-STS765B

Chuanhai Liu, Ryan Martin

引用次数: 8

The Box–Cox Transformation: Review and Extensions Box-Cox转换:回顾和扩展

IF 5.7 1区数学

Statistical Science Pub Date : 2021-05-01 DOI: 10.1214/20-STS778

A. Atkinson, M. Riani, A. Corbellini

引用次数: 44

A selective overview of deep learning. 深度学习的选择性概述。

IF 5.7 1区数学

Statistical Science Pub Date : 2021-05-01 Epub Date: 2020-04-19 DOI: 10.1214/20-sts783

Jianqing Fan, Cong Ma, Yiqiao Zhong

{"title":"A selective overview of deep learning.","authors":"Jianqing Fan, Cong Ma, Yiqiao Zhong","doi":"10.1214/20-sts783","DOIUrl":"10.1214/20-sts783","url":null,"abstract":"<p><p>Deep learning has achieved tremendous success in recent years. In simple words, deep learning uses the composition of many nonlinear functions to model the complex dependency between input features and labels. While neural networks have a long history, recent advances have greatly improved their performance in computer vision, natural language processing, etc. From the statistical and scientific perspective, it is natural to ask: What is deep learning? What are the new characteristics of deep learning, compared with classical methods? What are the theoretical foundations of deep learning? To answer these questions, we introduce common neural network models (e.g., convolutional neural nets, recurrent neural nets, generative adversarial nets) and training techniques (e.g., stochastic gradient descent, dropout, batch normalization) from a statistical point of view. Along the way, we highlight new characteristics of deep learning (including depth and over-parametrization) and explain their practical and theoretical benefits. We also sample recent results on theories of deep learning, many of which are only suggestive. While a complete understanding of deep learning remains elusive, we hope that our perspectives and discussions serve as a stimulus for new statistical research.</p>","PeriodicalId":51172,"journal":{"name":"Statistical Science","volume":"36 2","pages":"264-290"},"PeriodicalIF":5.7,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8300482/pdf/nihms-1639566.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39219267","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0