Publication

Publication and presentation in both English and Japanese.

Publication

Papers

  • Nakakita, S.H., Kaino, Y., and Uchida, M. (2020). Quasi-likelihood analysis and Bayes-type estimators of an ergodic diffusion plus noise. To appear in Annals of the Institute of Statistical Mathematics.

  • 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.

Preprints

  • Nakakita, S.H., and Uchida, M. (2017). Adaptive estimation and noise detection for an ergodic diffusion with observational noises. arXiv:1711.04462 [math.ST].

Theses

  • Nakakita, S.H. (2018). Statistical inference and noise detection for ergodic diffusions plus noise (master's thesis). Osaka University, Toyonaka, Japan.

  • Nakakita, S.H. (2016). Model averaging and dynamic regression by Kalman filter with the application to Japanese macroeconomic forecast (bachelor's thesis). Akita International University, Akita, Japan.

Presentation

Presentation in English

  • 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, September). Test theory for noisily observed diffusion processes. Presentation at Japanese Joint Statistical Meeting 2019, Shiga University, Hikone.

  • 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.

  • Nakakita, S.H., and Uchida, M. (2018, September). Adaptive maximum-likelihood-type estimation for discretely observed diffusion processes with observational noise. Presentation at Japanese Joint Statistical Meeting 2018, Chuo University, Tokyo.

Presentation in Japanese

  • Nakakita, S.H. (2020, September). 時間積分データに基づく確率微分方程式の統計モデリングと 脳波データ解析への応用 [statistical modelling with stochastic differential equations based on time-itegrated data and its application to EEG data analysis]. Presentation at Japanese Joint Statistical Meeting 2020.

  • Nakakita, S.H. (2019, October). 脳波データの確率微分方程式モデリングにおける時間積分検出問題 [time-integration detection problem in modelling of EEG data with stochastic differential equations]. Poster presentation at 日本数学会異分野・異業種研究交流会2019 [interdisciplinary and inter-industrial research meeting of the Mathematical Society of Japan 2019]. The University of Tokyo, Tokyo.

  • Nakakita, S.H., Kaino, Y., and Uchida, M. (2019, September). ノイズ付き拡散過程の疑似尤度解析 [quasi-likelihood analysis for noisily observed diffusion processes]. Presentation at 日本数学会・2019年度秋季総合分科会 [The Mathematical Society of Japan Autumn Meeting 2019]. Kanazawa University, Kanazawa.

  • Nakakita, S.H. (2019, August). ノイズ付き拡散過程の統計的検定 [statistical test for diffusion processes with noise]. Presentation at 統計サマーセミナー2019 [summer seminar for statistics 2019]. Kokumin-shukusha Hibiki, Munakata.

  • Nakakita, S.H. (2019, March). diffusion-plus-noiseモデルの疑似尤度解析 [quasi-likelihood analysis for diffusion-plus-noise model]. Poster presentation at 第13回日本統計学会春季集会 [13th Japan Statistical Society Spring Meeting], Nihon University, Tokyo.

  • Nakakita, S.H. (2018, November). 観測誤差付き拡散過程の疑似尤度解析 [quasi-likelihood analysis for diffusion processes with observation noise]. Poster presentation at 日本数学会異分野・異業種研究交流会2018 [interdisciplinary and inter-industrial research meeting of the Mathematical Society of Japan 2018]. Meiji University, Tokyo.

  • Nakakita, S.H. (2018, March). diffusion-plus-noiseモデルの統計的仮説検定 [statistical hypothesis testing for diffusion-plus-noise model]. Poster presentation at 第12回日本統計学会春季集会 [12th Japan Statistical Society Spring Meeting], Waseda University, Tokyo.

  • Nakakita, S.H. (2018, January). diffusion-plus-noiseモデルのパラメトリック推定 [parametric estimation for diffusion-plus-noise model]. Presentation at 確率過程の統計推測の最近の展開 [recent development of statistical inference for stochastic processes]. University of Tokyo, Tokyo.

  • Nakakita, S.H. (2018, January). diffusion-plus-noiseモデルの統計的推測理論 [statistical inference theory for diffusion-plus-noise model]. Poster presentation at 数学パワーが世界を変える2018 [the power of mathematics changes the world 2018], Akiba hall, Tokyo.

  • Nakakita, S.H. (2017, November). 観測ノイズ付き拡散過程に対する適応的推定 [adaptive estimation for diffusion processes with observation noise]. Poster presentation at 日本数学会異分野・異業種研究交流会2017 [interdisciplinary and inter-industrial research meeting of the Mathematical Society of Japan 2017]. Meiji University, Tokyo.

  • Nakakita, S.H. (2017, September). adaBayes関数の不具合及び修正に関して [some bugs and fixes in adaBayes function]. Presentation at 第3回YUIMAユーザー会ユース [the 3rd YUIMA user youth]. University of Tokyo, Tokyo.

  • Nakakita, S.H. (2017, August). 観測ノイズ付き拡散過程の統計的推測 [statistical inference for diffusion processes with observation noise]. Presentation at 統計サマーセミナー2017 [summer seminar for statistics 2017]. Kinugawa park hotels, Nikko.