Publication

Publication

Preprints

• Nakakita, S. (2023). Non-asymptotic analysis of Langevin-type Monte Carlo algorithms. arXiv:2303.12407 [math.ST]

• Nakakita, S. (2022). Parametric estimation of stochastic differential equations via online gradient descent. arXiv:2210.08800 [math.ST]

• Nakakita, S., and Imaizumi, M. (2022). Benign Overfitting in Time Series Linear Model with Over-Parameterization. arXiv:2204.08369 [math.ST]

Papers

• Nakakita, S., Alquier, P., and Imaizumi, M. (2024). Dimension-free Bounds for Sums of Dependent Matrices and Operators with Heavy-Tailed Distributions. Electronic Journal of Statistics, 18(1): 1130–1159.

• Nakakita, S. H., Kaino, Y., and Uchida, M. (2021). Quasi-likelihood analysis and Bayes-type estimators of an ergodic diffusion plus noise. Annals of the Institute of Statistical Mathematics, 73(1), 177–225.

• Nakakita, S. H., and Uchida, M. (2020). Inference for convolutionally observed diffusion processes. Entropy, 22(9), 1031.

• Kaino, Y., Nakakita, S. H., and Uchida, M. (2020). Hybrid estimation for ergodic diffusion processes based on noisy discrete observations. Statistical Inference for Stochastic Processes, 23(1), 171-198.

• Nakakita, S. H., and Uchida, M. (2019b). Adaptive test for ergodic diffusions plus noise. Journal of Statistical Planning and Inference, 203, 131–150.

• Nakakita, S. H., and Uchida, M. (2019a). Inference for ergodic diffusions plus noise. Scandinavian Journal of Statistics, 46(2), 470–516.

Presentation

• Nakakita, S. (2023, August). Online parametric estimation of stochastic differential equations with discrete observations. Invited presentation at the 10th International Congress on Industrial and Applied Mathematics (ICIAM 2023). Waseda University, Tokyo.

• Nakakita, S. (2023, August). Langevin-type Monte Carlo algorithms for weakly differentiable non-convex potentials. Presentation at the 6th International Conference on Econometrics and Statistics (EcoSta 2023). Waseda University, Tokyo.

• Nakakita, S. (2023, March). Parametric estimation of ergodic diffusion processes by online gradient descent. DYNSTOCH 2023 – Workshop on Statistical Methods for Dynamical Stochastic Models. Imperial College London, London.

• Nakakita, S. (2022, December). Estimation of diffusion processes by online gradient descent. Invited presentation at the 15th International Conference of the European Consortium for Informatics and Mathematics Working Group on Computational and Methodological Statistics (CMStatistics 2022). King's College London, London.

• Nakakita, S., and Imaizumi, M. (2022, June). Benign overfitting in stochastic regression. Invited presentation at the 5th International Conference on Econometrics and Statistics (EcoSta 2022). Ryukoku University, Kyoto.

• Nakakita, S., and Imaizumi, M. (2022, April). Benign Overfitting in Overparameterized Time Series Models. Presentation at Workshop on the Theory of Overparameterized Machine Learning 2022 (TOPML 2022).

• Nakakita, S. H., Kaino, Y., and Uchida, M. (2021, July). Adaptive Bayes-type estimators for noisily observed ergodic diffusion processes. Presentation at the 63rd International Statistical Institute World Statistics Congress 2021 (ISI WSC 2021).

• Nakakita, S. H., and Uchida, M. (2020, August). Inference for an ergodic diffusion with smooth observations. Presentation at Bernoulli-IMS One World Symposium 2020. 

• Nakakita, S. H., and Uchida, M. (2019, August). Adaptive estimators for noisily observed diffusion processes. Presentation at the 62nd International Statistical Institute World Statistics Congress 2019 (ISI WSC 2019), Kuala Lumpur Convention Centre, Kuala Lumpur.

• Nakakita, S. H., and Uchida, M. (2018, December). Adaptive maximum-likelihood-type estimation for discretely and noisily observed diffusion processes. Presentation at the 11th International Conference of the European Consortium for Informatics and Mathematics Working Group on Computational and Methodological Statistics (CMStatistics 2018). University of Pisa, Pisa.