Statistical Science最新文献_第8页

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":"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.","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

Robust high dimensional factor models with applications to statistical machine learning. 鲁棒高维因子模型及其在统计机器学习中的应用。

IF 3.9 1区数学

Statistical Science Pub Date : 2021-05-01 Epub Date: 2021-04-19 DOI: 10.1214/20-sts785

Jianqing Fan, Kaizheng Wang, Yiqiao Zhong, Ziwei Zhu

{"title":"Robust high dimensional factor models with applications to statistical machine learning.","authors":"Jianqing Fan, Kaizheng Wang, Yiqiao Zhong, Ziwei Zhu","doi":"10.1214/20-sts785","DOIUrl":"10.1214/20-sts785","url":null,"abstract":"Factor models are a class of powerful statistical models that have been widely used to deal with dependent measurements that arise frequently from various applications from genomics and neuroscience to economics and finance. As data are collected at an ever-growing scale, statistical machine learning faces some new challenges: high dimensionality, strong dependence among observed variables, heavy-tailed variables and heterogeneity. High-dimensional robust factor analysis serves as a powerful toolkit to conquer these challenges. This paper gives a selective overview on recent advance on high-dimensional factor models and their applications to statistics including Factor-Adjusted Robust Model selection (FarmSelect) and Factor-Adjusted Robust Multiple testing (FarmTest). We show that classical methods, especially principal component analysis (PCA), can be tailored to many new problems and provide powerful tools for statistical estimation and inference. We highlight PCA and its connections to matrix perturbation theory, robust statistics, random projection, false discovery rate, etc., and illustrate through several applications how insights from these fields yield solutions to modern challenges. We also present far-reaching connections between factor models and popular statistical learning problems, including network analysis and low-rank matrix recovery.","PeriodicalId":51172,"journal":{"name":"Statistical Science","volume":"36 2","pages":"303-327"},"PeriodicalIF":3.9,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8315369/pdf/nihms-1639567.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39254018","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

Comment: On the History and Limitations of Probability Updating 评论:概率更新的历史和局限性

IF 5.7 1区数学

Statistical Science Pub Date : 2021-04-01 DOI: 10.1214/21-STS765A

G. Shafer

引用次数: 0

Comment: Moving Beyond Sets of Probabilities 评论：超越概率集

IF 5.7 1区数学

Statistical Science Pub Date : 2021-04-01 DOI: 10.1214/21-STS765C

G. Wheeler

引用次数: 0

Stochastic Approximation: From Statistical Origin to Big-Data, Multidisciplinary Applications 随机逼近:从统计起源到大数据，多学科应用