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05/27/2023
High-dimensional Central Limit Theorems by Stein's Method in the Degenerate Case
In the literature of high-dimensional central limit theorems, there is a...
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07/06/2022
Adaptive deep learning for nonparametric time series regression
In this paper, we develop a general theory for adaptive nonparametric es...
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05/19/2022
High-dimensional Data Bootstrap
This article reviews recent progress in high-dimensional bootstrap. We f...
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12/26/2021
Drift estimation for a multi-dimensional diffusion process using deep neural networks
Recently, many studies have shed light on the high adaptivity of deep ne...
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12/17/2020
Nearly optimal central limit theorem and bootstrap approximations in high dimensions
In this paper, we derive new, nearly optimal bounds for the Gaussian app...
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09/01/2020
Large-dimensional Central Limit Theorem with Fourth-moment Error Bounds on Convex Sets and Balls
We prove the large-dimensional Gaussian approximation of a sum of n inde...
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01/29/2020
High-dimensional Central Limit Theorems by Stein's Method
We obtain explicit error bounds for the standard d-dimensional normal ap...
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12/22/2019
Improved Central Limit Theorem and bootstrap approximations in high dimensions
This paper deals with the Gaussian and bootstrap approximations to the d...
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11/01/2019
Notes on the dimension dependence in high-dimensional central limit theorems for hyperrectangles
Let X_1,...,X_n be independent centered random vectors in R^d. This pape...
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05/04/2019
De-biased graphical Lasso for high-frequency data
This paper develops a new statistical inference theory for the precision...
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02/11/2019
High-dimensional central limit theorems for homogeneous sums
This paper develops a quantitative version of de Jong's central limit th...
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06/13/2018
Mixed-normal limit theorems for multiple Skorohod integrals in high-dimensions, with application to realized covariance
This paper develops mixed-normal approximations for probabilities that v...
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11/09/2017