Gaussian Multiplier Bootstrap Procedure for the $k$th Largest Coordinate of High-Dimensional Statistics

We consider the problem of Gaussian multiplier bootstrap procedures for the $k$th largest statistics and functions of the top $k$ order statistics, which are commonly encountered in high-dimensional statistical inference.

Such a problem has been studied previously for $k=1$ (i.e., maxima).

However, in many applications, a general $k$ ($k\geq 1$) is of great interest.

We provide the upper bounds for the errors between Gaussian approximations and Gaussian multiplier approximations.

The dimension $p$ is allowed to be larger than the sample size $n$.

The effectiveness of the proposed methods is demonstrated via the computer numerical results and a real-world data analysis.