Improved Central Limit Theorem and bootstrap approximations in high dimensions

• Tác giả: Victor Chernozhukov, Denis Chetverikov, Kengo Kato, Yuta Koike
• Ngôn ngữ: eng
• Ký hiệu phân loại: 511.4 Approximations formerly also 513.24 and expansions
• Thông tin xuất bản: 2019
• Bộ sưu tập: Metadata
• ID: 163753

Tóm tắt

Comment: 63 pagesThis paper deals with the Gaussian and bootstrap approximations to the distribution of the max statistic in high dimensions. This statistic takes the form of the maximum over components of the sum of independent random vectors and its distribution plays a key role in many high-dimensional econometric problems. Using a novel iterative randomized Lindeberg method, the paper derives new bounds for the distributional approximation errors. These new bounds substantially improve upon existing ones and simultaneously allow for a larger class of bootstrap methods.