Improved central limit theorem and bootstrap approximations in high dimensions

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