Presentation

The presenter is underlined.

2024 (including scheduled)

• Kurisu, D.  TBA. CMStatistics2024, December 14-16, 2024, King's College London, UK (invited/hybrid). 

• Kurisu, D.  TBA. The German-Japanese Fall School "Time series, random fields, and beyond", September 21- 29, 2024, University of Ulm, Ulm, Germany (Invited). 

• Kurisu, D.  and Otsu, T. Nonparametric inference on Frechet mean and related population objects on manifolds. EcoSta2024, July 17-19, 2024, Beijing Normal University, Beijing, China (invited/hybrid).

• Kurisu, D., Kato, K., and Shao, X. Spatially dependent wild bootstrap for high-dimensional spatial data. IMS-APRM2024, January 4-7, 2024, Melbourne, Australia (invited). 

2023 (including scheduled)

• Kurisu, D.  and Otsu, T.  Nonparametric inference on intrinsic means. CMStatistics2023, December 16-18, 2023, University of Applied Sciences, Berlin, Germany (hybrid/invited). 

• Kurisu, D., Fukami, R., and Koike, Y. Deep learning for nonstationary and nonlinear time series models. Statistics Colloquium, December 4, Center for Statistical Science, Tsinghua University, Beijing, China (invited/online). 

• Kurisu, D.  and Otsu, T. Prediction, inference, and hypothesis testing of non-Euclidean data. Statistics Seminar, October 5, 2023, Department of Statistics, University of California, Davis, USA. 

• Kurisu, D.  and Otsu, T.  Prediction and nonparametric inference on random objects. Econometrics Seminar, September 15, 2023, Kyoto Institute of  Economic Research, Japan (invited).

• Kurisu, D.  and Otsu, T. Subsampling inference for nonparametric extremal conditional quantiles. ICIAM2023, August 20-25, 2023, Waseda University, Japan (invited).  

• Kurisu, D., Fukami, R., and Koike, Y.  Adaptive deep learning for nonlinear time series models. JAFEE-ISM International Symposium on Quantitative Finance, August 18-19, 2023, Chuo University, Japan. (Invited)

• Kurisu, D. and Otsu, T. Model averaging for global Fréchet regression. EcoSta2023, August 1-3, 2023, Waseda University, Japan (invited).

• Ishihara, T., Kurisu, D., Matsuda, Y. and Sawada, Y. Local-polynomial estimation for multivariate regression discontinuity designs. 2023 Asia Meeting of the Econometric Society, July 28-30, Nanyang Technology University, Singapore. 

• Kurisu, D. and Otsu, T. Model averaging and empirical likelihood for non-Euclidean data. BayesCompJp, July 10, ISM, Japan (invited).

2022

• Kurisu, D., Fukami, R., and Koike, Y.  Adaptive deep learning for nonparametric time series regression. CMStatistics2022, December 17-19, 2022, King's College London, UK (hybrid). 

• Ishihara, T. and Kurisu, D. Shrinkage methods for treatment choice. 3rd Tohoku-ISM-UUlm Joint Workshop, October 12-14, 2022, Tohoku University, Japan.

• Ishihara, T., Kurisu, D., Matsuda, Y., and Sawada, M. Spatial regression discontinuity designs. 3rd Tohoku-ISM-UUlm Joint Workshop, October 12-14, 2022, Tohoku University, Japan. 

• Kurisu, D. Nonparametric regression for locally stationary random fields on R^d. 3rd Tohoku-ISM-UUlm Joint Workshop, October 12-14, 2022, Tohoku University, Japan. 

• Ishihara, T. and Kurisu, D. Shrinkage methods for treatment choice. 2022 Asia Meeting of the Econometric Society (AMES), East and South Asia, August 8-10, 2022, Keio University and University of Tokyo, Japan (hybrid). 

• Kurisu, D., Kato, K., and Shao, X. Gaussian approximation and spatially dependent wild bootstrap for high-dimensional spatial data. EcoSta2022, June 4-6, 2022, Ryukoku University, Kyoto, Japan (invited/hybrid). 

• Kurisu, D., Ishihara, T. and Sugasawa, S. Adaptively robust small area estimation. SAE2022, May 23-27, 2022,  University of Maryland, College Park, USA (hybrid). 

2021

• ICSA 2021 China Conference.

• Kurisu, D. On the estimation of nonstationary functional data. CMStatistics2021, December 18-20, 2021, King's College London, London, UK (invited/hybrid). 

• Kurisu, D. On the estimation of nonstationary functional time series.  CSA-KSS-JSS joint international session. December 10, 2021 (invited/online).

• Kurisu, D., Kato, K., and Shao, X. Wild bootstrap for high-dimensional spatial data. Bernoulli-IMS 10th World Congress, July 19-23, 2021 (online). 

• Kurisu, D., Kato, K., and Shao, X. Gaussian approximation and bootstrap for high-dimensional spatial data. 63rd ISI World Statistics Congress 2021, July 11-16, 2021 (online). 

• Kurisu, D., Kato, K., and Shao, X. Spatially dependent wild bootstrap for high-dimensional spatial data. XV World Conference of the Spatial Econometrics Association. May 26-28, 2021 (online).

• Kurisu, D., Kato, K., and Shao, X. Spatially dependent wild bootstrap for high-dimensional spatial data. University of Alberta Statistics Seminar, March 4, 2021 (invited/online).

2020

• Kurisu, D. and Kato, K. Wild bootstrap for spatio-temporal data. CMStatistics2020, December 19-21, 2020 (online).

2019

• Kunitomo, N. and Kurisu, D. Detecting the number of factors of quadratic variation in the presence of microstructure noise. CMStatistics2019, December 14-16, 2019, Senate House, University of London, London, UK. (invited)

• Kato, K. and Kurisu, D. Bootstrap confidence bands for spectral estimation of Lévy densities under high-frequency observations. EcoSta2019, June 25-27, 2019, National Chung Hsing University, Taiwan.

• Kunitomo, N. and Kurisu, D. Detecting the number of factors of quadratic variation in the presence of microstructure noise. SETA2019, June1-2, 2019, Osaka University, Osaka, Japan.

• Kurisu, D. Nonparametric inference for Lévy models. ICMMA2018, February 11-13, 2019, Meiji University, Tokyo, Japan. (invited)

2018

• Kurisu, D. Nonparametric inference on Lévy-driven Ornstein-Uhlenbeck processes. CMStatistics2018, December 14-16, 2018, University of Pisa, Pisa, Italy.

• Kato, K. and Kurisu, D. Bootstrap confidence bands for Lévy densities under high-frequency observations and its application to financial data. STICERD Econometrics Seminar, November 26-30, 2018, London School of Economics, London, UK. 

• Kato, K. and Kurisu, D. Bootstrap confidence bands for spectral estimation of Lévy densities under high-frequency observations. RSFAS Seminar, August 20-24, 2018, College of Business and Economics, Australian National University, Canberra, Australia. (invited) 

• Kurisu, D. Nonparametric inference on Lévy measures of Lévy-driven Ornstein-Uhlenbeck processes. JSM2018, July 28-August 2, 2018, Vancouver Convention Centre, Vancouver, Canada. 

• Kurisu, D. Nonparametric inference on Lévy-driven Ornstein-Uhlenbeck processes under discrete observations. IMS-APRM2018, June 26-29, 2018, National University of Singapore, Singapore. (invited)

• Kato, K. and Kurisu, D. Bootstrap confidence bands for spectral estimation of Lévy densities under high-frequency observations. Workshop on econometric analysis for big data, March 13, 2018, Tohoku University, Sendai, Japan. (invited)

2017

• Kato, K. and Kurisu, D. Bootstrap confidence bands for spectral estimation of Lévy densities under high-frequency observations. CMStatistics2017, December 16-18, 2017, Senate House, University of London, London, UK. 

• Kunitomo, N., Kurisu, D., and Awaya, N. The simultaneous multivariate Hawkes-type point processes and their applications to financial markets. ISI2017, July 16-21, 2017, The Mansour Eddahbi Hotek & Palais des Congres, Marrakech, Morocco.

• Kato, K. and Kurisu, D. Bootstrap confidence bands for Lévy densities under high-frequency observations. EcoSta2017, June 15-17, 2017, The Hong Kong University of Science and Technology, Hong Kong. 

• Kato, K. and Kurisu, D. Bootstrap confidence bands for spectral estimation of Lévy densities under high-frequency observations. CeMMAP conference on advances in econometrics, June 7-8, 2017, Shanghai Jiao Tong University, Shanghai, China. (invited)

2016

• Kunitomo, N., Kurisu, D., and Awaya, N. Simultaneous event multivariate point process models with applications to causality analysis of financial markets. CFE2016, December 9-12, 2016, University of Seville, Spain.   

• Kunitomo, N., Ehara, A., and Kurisu, D.  Causality analysis of financial markets by using extended mutually exciting processes. KSS Fall Conference, November 4-5, 2016, Statistical Training Center, Daejeon, Korea. (invited)

• Kunitomo, N. and Kurisu, D. Small noise asymptotics in high-frequency financial econometrics. JSM2016, July 30-August 4, 2016, McCormick Place Convention Center, Chicago, USA. 

2015

• Kunitomo, N. and Kurisu, D. On effects of jump and noise in high-frequency financial econometrics. CFE2015, December 12-14, 2015, Senate House, University of London, London, UK.