7 citations · 13 across the 14 of their papers we have counts for
10 papers · 1 filter
Local polynomial trend regression for spatial data on
Daisuke Kurisu, Yasumasa Matsuda
This paper develops a general asymptotic theory of local polynomial (LP) regression for spatial data observed at irregularly spaced locations in a sampling region $R_n \subset \mat…
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…
On the estimation of locally stationary functional time series
Daisuke Kurisu
This study develops an asymptotic theory for estimating the time-varying characteristics of locally stationary functional time series (LSFTS). We investigate a kernel-based method…
Nonparametric regression for locally stationary functional time series
Daisuke Kurisu
In this study, we develop an asymptotic theory of nonparametric regression for a locally stationary functional time series. First, we introduce the notion of a locally stationary f…
Gaussian approximation and spatially dependent wild bootstrap for high-dimensional spatial data
Daisuke Kurisu, Kengo Kato, Xiaofeng Shao
In this paper, we establish a high-dimensional CLT for the sample mean of -dimensional spatial data observed over irregularly spaced sampling sites in , allowing t…
Nonparametric regression for locally stationary random fields under stochastic sampling design
Daisuke Kurisu
In this study, we develop an asymptotic theory of nonparametric regression for locally stationary random fields (LSRFs) in $\math…
On nonparametric inference for spatial regression models under domain expanding and infill asymptotics
Daisuke Kurisu
In this paper, we develop nonparametric inference on spatial regression models as an extension of Lu and Tj\ostheim(2014), which develops nonparametric inference on density functio…
Bootstrap confidence bands for spectral estimation of Lévy densities under high-frequency observations
Kengo Kato, Daisuke Kurisu
This paper develops bootstrap methods to construct uniform confidence bands for nonparametric spectral estimation of Lévy densities under high-frequency observations. We assume tha…
Discretization of Self-Exciting Peaks Over Threshold Models
Daisuke Kurisu
In this paper, a framework on a discrete observation of (marked) point processes under the high-frequency observation is developed. Based on this framework, we first clarify the re…
Power variations and testing for co-jumps: the small noise approach
Daisuke Kurisu
In this paper we study the effects of noise on the bipower variation (BPV), realized volatility (RV) and testing for co-jumps in high-frequency data under the small noise framework…