25 citations · 89 across the 33 of their papers we have counts for
34 papers
Martingale central limit theorems in -Wasserstein distance
Xiao Fang, Yuta Koike, Zi-Yao Su
We obtain multivariate martingale central limit theorems in -Wasserstein distance with respect to the norm in for and , which…
Wasserstein bounds for denoising diffusion probabilistic models via the Föllmer process
Yuta Koike
This paper studies sampling error bounds for denoising diffusion probabilistic models (DDPMs) in the 2-Wasserstein distance. Our contributions are threefold. (i) Under general Lips…
Connections between the Föllmer process and the denoising diffusion probabilistic model
Yuta Koike
The Föllmer process is a Brownian motion conditioned to have a pre-specified distribution at time 1. This process can be interpreted as an ``augmented'' time-compressed version of…
On lead-lag estimation of non-synchronously observed point processes
Takaaki Shiotani, Takaki Hayashi, Yuta Koike
This paper introduces a new theoretical framework for analyzing lead-lag relationships between point processes, with a special focus on applications to high-frequency financial dat…
Gaussian Approximation for High-Dimensional -statistics with Size-Dependent Kernels
Shunsuke Imai, Yuta Koike
Motivated by small bandwidth asymptotics for kernel-based semiparametric estimators in econometrics, this paper establishes Gaussian approximation results for high-dimensional fixe…
High-dimensional bootstrap and asymptotic expansion
Yuta Koike
The recent seminal work of Chernozhukov, Chetverikov and Kato has shown that bootstrap approximation for the maximum of a sum of independent random vectors is justified even when t…
Spectral norm bounds for high-dimensional realized covariance matrices and application to weak factor models
Yuta Koike
Motivated by statistical analysis of latent factor models for high-frequency financial data, we develop sharp upper bounds for the spectral norm of the realized covariance matrix o…
High-dimensional Central Limit Theorems by Stein's Method in the Degenerate Case
Xiao Fang, Yuta Koike, Song-Hao Liu +1
In the literature of high-dimensional central limit theorems, there is a gap between results for general limiting correlation matrix and the strongly non-degenerate case. For t…
Sharp High-dimensional Central Limit Theorems for Log-concave Distributions
Xiao Fang, Yuta Koike
Let be i.i.d. log-concave random vectors in with mean 0 and covariance matrix . We study the problem of quantifying the normal approximation error…
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…
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…
Drift estimation for a multi-dimensional diffusion process using deep neural networks
Akihiro Oga, Yuta Koike
Recently, many studies have shed light on the high adaptivity of deep neural network methods in nonparametric regression models, and their superior performance has been established…
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…
Improved Central Limit Theorem and bootstrap approximations in high dimensions
Victor Chernozhukov, Denis Chetverikov, Kengo Kato +1
This paper deals with the Gaussian and bootstrap approximations to the distribution of the max statistic in high dimensions. This statistic takes the form of the maximum over compo…
Notes on the dimension dependence in high-dimensional central limit theorems for hyperrectangles
Yuta Koike
Let be independent centered random vectors in . This paper shows that, even when may grow with , the probability $P(n^{-1/2}\sum_{i=1}^nX_i\in…
De-biased graphical Lasso for high-frequency data
Yuta Koike
This paper develops a new statistical inference theory for the precision matrix of high-frequency data in a high-dimensional setting. The focus is not only on point estimation but…
High-dimensional central limit theorems for homogeneous sums
Yuta Koike
This paper develops a quantitative version of de Jong's central limit theorem for homogeneous sums in a high-dimensional setting. More precisely, under appropriate moment assumptio…
Mixed-normal limit theorems for multiple Skorohod integrals in high-dimensions, with application to realized covariance
Yuta Koike
This paper develops mixed-normal approximations for probabilities that vectors of multiple Skorohod integrals belong to random convex polytopes when the dimensions of the vectors p…
No arbitrage and lead-lag relationships
Takaki Hayashi, Yuta Koike
The existence of time-lagged cross-correlations between the returns of a pair of assets, which is known as the lead-lag relationship, is a well-known stylized fact in financial eco…
Oracle inequalities for sign constrained generalized linear models
Yuta Koike, Yuta Tanoue
High-dimensional data have recently been analyzed because of data collection technology evolution. Although many methods have been developed to gain sparse recovery in the past two…