7 citations · 13 across the 14 of their papers we have counts for
14 papers
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…
Shrinkage Methods for Treatment Choice
Takuya Ishihara, Daisuke Kurisu
This study examines the problem of determining whether to treat individuals based on observed covariates. The most common decision rule is the conditional empirical success (CES) r…
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…
Adaptively Robust Small Area Estimation: Balancing Robustness and Efficiency of Empirical Bayes Confidence Intervals
Daisuke Kurisu, Takuya Ishihara, Shonosuke Sugasawa
Empirical Bayes small area estimation based on the well-known Fay-Herriot model may produce unreliable estimates when outlying areas exist. Existing robust methods against outliers…
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…