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Nearly optimal central limit theorem and bootstrap approximations in high dimensions
In this paper, we derive new, nearly optimal bounds for the Gaussian approximation to scaled averages of $n$ independent high-dimensional centered random vectors $X_1,\dots, X_n$ over the class of rectangles in the case when the covariance matrix of the scaled average is non-degenerate.
Probability Statistics Theory Statistics Theory 60F05, 62E17
Adaptive deep learning for nonlinear time series models
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 (DNNs).
