High-Dimensional Data Bootstrap

被引：16

作者：

Chernozhukov, Victor ^{[1
,2
]}

Chetverikov, Denis ^{[3
]}

Kato, Kengo ^{[4
]}

Koike, Yuta ^{[5
,6
]}

机构：

[1] MIT, Dept Econ, Cambridge, MA 02139 USA

[2] MIT, Ctr Stat & Data Sci, Cambridge, MA 02139 USA

[3] Univ Calif Los Angeles, Dept Econ, Los Angeles, CA 90024 USA

[4] Cornell Univ, Dept Stat & Data Sci, Ithaca, NY USA

[5] Univ Tokyo, Math & Informat Ctr, Tokyo, Japan

[6] Univ Tokyo, Grad Sch Math Sci, Tokyo, Japan

来源：

ANNUAL REVIEW OF STATISTICS AND ITS APPLICATION | 2023年 / 10卷

基金：

美国国家科学基金会; 日本科学技术振兴机构;

关键词：

empirical bootstrap; high-dimensional central limit theorem; multiple testing; multiplier bootstrap; simultaneous inference; MULTIVARIATE NORMAL APPROXIMATION; POST-SELECTION INFERENCE; CENTRAL LIMIT-THEOREMS; GAUSSIAN APPROXIMATION; CONFIDENCE-REGIONS; U-STATISTICS; HYPOTHESIS TESTS; STEINS METHOD; CONVERGENCE; PARAMETERS;

D O I：

10.1146/annurev-statistics-040120-022239

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

This article reviews recent progress in high-dimensional bootstrap. We first review high-dimensional central limit theorems for distributions of sample mean vectors over the rectangles, bootstrap consistency results in high dimensions, and key techniques used to establish those results. We then review selected applications of high-dimensional bootstrap: construction of simultaneous confidence sets for high-dimensional vector parameters, multiple hypothesis testing via step-down, postselection inference, intersection bounds for partially identified parameters, and inference on best policies in policy evaluation. Finally, we also comment on a couple of future research directions.

引用

页码：427 / 449

页数：23

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