1 citations · 1 across the 3 of their papers we have counts for
10 papers
From -Wasserstein Bounds to Moderate Deviations
Xiao Fang, Yuta Koike
We use a new method via -Wasserstein bounds to prove Cramér-type moderate deviations in (multivariate) normal approximations. In the classical setting that is a standardized…
High-dimensional Data Bootstrap
Victor Chernozhukov, Denis Chetverikov, Kengo Kato +1
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 rectan…
Nearly optimal central limit theorem and bootstrap approximations in high dimensions
Victor Chernozhukov, Denis Chetverikov, Yuta Koike
In this paper, we derive new, nearly optimal bounds for the Gaussian approximation to scaled averages of independent high-dimensional centered random vectors ov…
Large-dimensional Central Limit Theorem with Fourth-moment Error Bounds on Convex Sets and Balls
Xiao Fang, Yuta Koike
We prove the large-dimensional Gaussian approximation of a sum of independent random vectors in together with fourth-moment error bounds on convex sets and Eucli…
New error bounds in multivariate normal approximations via exchangeable pairs with applications to Wishart matrices and fourth moment theorems
Xiao Fang, Yuta Koike
We extend Stein's celebrated Wasserstein bound for normal approximation via exchangeable pairs to the multi-dimensional setting. As an intermediate step, we exploit the symmetry of…
High-dimensional Central Limit Theorems by Stein's Method
Xiao Fang, Yuta Koike
We obtain explicit error bounds for the -dimensional normal approximation on hyperrectangles for a random vector that has a Stein kernel, or admits an exchangeable pair coupling…