High-Dimensional Data Bootstrap

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  • Victor Chernozhukov
    Department of Economics and Center for Statistics and Data Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA;
  • Denis Chetverikov
    Department of Economics, University of California, Los Angeles, California, USA;
  • Kengo Kato
    Department of Statistics and Data Science, Cornell University, Ithaca, New York, USA;
  • Yuta Koike
    Mathematics and Informatics Center and Graduate School of Mathematical Sciences, The University of Tokyo, Tokyo, Japan;

書誌事項

公開日
2023-03-10
資源種別
journal article
権利情報
  • http://creativecommons.org/licenses/by/4.0/
DOI
  • 10.1146/annurev-statistics-040120-022239
  • 10.48550/arxiv.2205.09691
公開者
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説明

<jats:p>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.</jats:p>

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