Yuta Koike

activity

20132026

most citedImproved Central Limit Theorem and bootstrap approximations in high dimensions

25 citations · 89 across the 33 of their papers we have counts for

collaborators

Showing 2022Show all

4 papers · 1 filter

math.PR2022

Sharp High-dimensional Central Limit Theorems for Log-concave Distributions

Xiao Fang, Yuta Koike

Let $X_{1}, \dots, X_{n}$ be i.i.d. log-concave random vectors in $R^{d}$ with mean 0 and covariance matrix $Σ$ . We study the problem of quantifying the normal approximation error…

math.ST2022

Adaptive deep learning for nonlinear time series models

Daisuke Kurisu, Riku Fukami, Yuta Koike

In this paper, we develop a general theory for adaptive nonparametric estimation of the mean function of a non-stationary and nonlinear time series model using deep neural networks…

math.PR2022

From $p$ -Wasserstein Bounds to Moderate Deviations

Xiao Fang, Yuta Koike

We use a new method via $p$ -Wasserstein bounds to prove Cramér-type moderate deviations in (multivariate) normal approximations. In the classical setting that $W$ is a standardized…

math.ST2022

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…

Sharp High-dimensional Central Limit Theorems for Log-concave Distributions

Adaptive deep learning for nonlinear time series models

From p-Wasserstein Bounds to Moderate Deviations

High-dimensional Data Bootstrap

From $p$ -Wasserstein Bounds to Moderate Deviations