Publication

Publication and presentation in both English and Japanese.

Publication

Papers

  • 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.

Dissertations and Theses

  • Nakakita, S. H. (2021). Statistical Inference for Noisily Observed Ergodic Diffusion Processes (doctoral dissertation). Osaka University, Toyonaka, Japan.

  • 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

  • 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, 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.