4 papers · 1 filter
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…
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…
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…