Research

Selected Publications 

2021 - 2025

1. Adaptive deep learning for nonlinear time series models (with R. Fukami and Y. Koike). [Link]  arXiv:2207.02546. accepted to Bernoulli. 

2. Local polynomial trend regression for spatial data on R^d (with Y. Matsuda). [Link] arXiv:2211.13467. accepted to Bernoulli. 

3. Subsampling inference for nonparametric extremal conditional quantiles (with T. Otsu). [Link] STICERD EPS EM616 accepted to Econometric Theory.

4. Gaussian approximation and spatially dependent wild bootstrap for high-dimensional spatial data (with K. Kato and X. Shao) [Link] arXiv:2103.10720. accepted to Journal of the American Statistical Association (T&M). 

5. Nonparametric regression for locally stationary functional time series. Electronic Journal of Statistics 16, 2022, 3973--3995. [Link] arXiv:2105.07613. 

6. On linearization of nonparametric deconvolution estimators for repeated measurements model (with T. Otsu). Journal of Multivariate Analysis 189, 2022, 104921. [Link]  STICERD EPS EM615

7. Nonparametric regression for locally stationary random fields under stochastic sampling design. Bernoulli 28, 2022, 1250--1275. [Link]  arXiv:2005.06371 

8. On the uniform convergence of deconvolution estimators from repeated measurements (with T. Otsu). Econometric Theory 38, 2022, 172--193. [Link] STICERD EPS EM604  

2018 - 2020

9. Inference on distribution functions under measurement error (with K. Adusumilli, T. Otsu, and Y.-J. Whang). Journal of Econometrics 215, 2020, 131--164. [Link]

10. Bootstrap confidence bands for spectral estimation of Lévy densities under high-frequency observations (with K. Kato). Stochastic Processes and their Applications 130, 2020, 1159--1205. [Link] arXiv:1705.00586 

11. Nonparametric inference on Lévy measures of compound Poisson-driven Ornstein-Uhlenbeck processes under macroscopic discrete observations. Electronic Journal of Statistics 13, 2019, 2521--2565. [Link] arXiv:1803.08671 

12. On nonparametric inference for spatial regression models under domain expanding and infill asymptotics. Statistics & Probability Letters 154, 2019, 108543. [Link] arXiv:1804.09402 

13. Power variations and testing for co-jumps: the small noise approach. Scandinavian Journal of Statistics 45, 2018, 482--512. [Link]

Working Papers

1. Geodesic analysis for non-Euclidean data (with Y. Zhou, T. Otsu, and H.-G. Müller).  coming soon.

2. Local-polynomial estimation for multivariate regression discontinuity designs (with M. Sawada, T. Ishihara, and Y. Matsuda). arXiv:2402.08941. submitted. 

3. Series ridge regression for spatial data on R^d (with Y. Matsuda). arXiv:2402.02773. submitted. 

4. Nonparametric causal inference with functional covariates (with T. Otsu and M. Xu). STICERD EPS EM631 R&R at Journal of Business & Economic Statistics. 

5. Hierarchical regression discontinuity design: Pursuing subgroup treatment effects (with S. Sugasawa and T. Ishihara). arXiv:2309.01404.  R code submitted. 

6. Empirical likelihood for manifolds (with T. Otsu). STICERD EPS EM632 submitted. 

7. Model averaging for global Fréchet regression (with T. Otsu). STICERD EPS EM628 submitted. 

8. Shrinkage methods for treatment choice (with T. Ishihara). arXiv:2210.17063. submitted. 

9. Adaptively robust small area estimation: Balancing robustness and efficiency of empirical Bayes confidence intervals (with T. Ishihara and S. Sugasawa).  arXiv:2108.11551.  R code submitted. 

10. On the estimation of locally stationary functional time series. arXiv:2105.11873.

Work in Progress 

• Spatial regression discontinuity designs (with T. Ishihara, Y. Matsuda, and M. Sawada).

• Uniform and weak convergence of boundary-corrected nonparametric estimates (with S. Kanaya). 

• Work on spatio-temporal data analysis (with Y. Matsuda).

• Work on functional data analysis (with Y. Terada).

• Work on deep learning (with Y. Terada).

• Work on random objects (with Y. Zhou, H.-G. Müller).

• Work on random objects (with Y. Zhou, T. Otsu,  H.-G. Müller).

• Work on graph neural network (with T. Otsu). 

• Work on clustering methods for quantile regression (with T. Ito).

• Work on directional data analysis (with T. Ito).   

• Work on nonstationary spatial data (with W. Wu).

• Work on statistical inference for high-dimensional time series (with Y. Koike).

Technical Reports/Notes

• Lévy-driven random fields and spatio-temporal models. PDF

Books/Translation Works

1. Gelman, A., Carlin, J.B., Stern, H.S., Dunson, D.B., Vehtari, A., and Rubin, D.B. (2013)  Bayesian Data Analysis 3rd Ed. (with S. Sugasawa, G. Kobayashi, Y. Kawakubo, and H. Tamae, translation into Japanese). Morikita Publising, 2024.  

2. Resnick, S.I. (2007) Heavy-Tail Phenomena: Probabilistic and Statistical Modeling (with N. Kunitomo, translation into Japanese). Asakura Publishing, 2021.

3. Separating information maximum likelihood method for high-frequency financial data (with N. Kunitomo and S. Sato). JSS Research Series in Statistics,  Springer, 2018.

Dissertation

• Essays on statistical inference for Ito-semimartingales under high-frequency observations. The University of Tokyo, 2018.