Nearly optimal central limit theorem and bootstrap approximations in high dimensions
Author:
Affiliation:
1. Department of Economics and Center for Statistics & Data Science, MIT
2. Department of Economics, UCLA
3. Mathematics and Informatics Center and Graduate School of Mathematical Sciences, University of Tokyo
Publisher
Institute of Mathematical Statistics
Subject
Statistics, Probability and Uncertainty,Statistics and Probability
Reference53 articles.
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3. Bobkov, S. G., Chistyakov, G. P. and Götze, F. (2013). Rate of convergence and Edgeworth-type expansion in the entropic central limit theorem. Ann. Probab. 41 2479–2512.
4. Chernozhukov, V., Chetverikov, D. and Kato, K. (2015). Comparison and anti-concentration bounds for maxima of Gaussian random vectors. Probab. Theory Related Fields 162 47–70.
5. Fathi, M. (2021). Higher-order Stein kernels for Gaussian approximation. Studia Math. 256 241–258.
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