2024 (Apr 2024 - Mar 2025)

  • Park, Y., Yoshida, N.: Asymptotic expansion for batched bandits. Statistical Inference for Stochastic Processes, to appear. (arXiv:2304.0417)
  • Yamagishi, H., Yoshida, N.: Asymptotic expansion of the quadratic variation of fractional stochastic differential equation. Stochastic Processes and their Applications, to appear
  • Yoshida, N.: Simplified quasi-likelihood analysis for a locally asymptotically quadratic random field. Annals of the Institute of Statistical Mathematics, to appear. 
  • Gloter, A., Yoshida, N.: Non-adaptive estimation for degenerate diffusion processes. Theory of Probability and Mathematical Statistics, 110, 75-99 (2024)
  • Kawamo, E. and Masuda, H.: On estimation of heavy-tailed stable linear regression. arXiv:2404.10448 (2024)

2023 (Apr 2023 - Mar 2024)

  • Masuda, H., Mercuri, L., and Uehara, Y.: Student t-Lévy regression model in YUIMA. arXiv:2403.12078
  • Ho, K.L.K. and Masuda, H.: Adaptive ridge approach to heteroscedastic regression. arXiv:2402.13642
  • Imamura, T., Masuda, H. and Tajima, H.: On local likelihood asymptotics for Gaussian mixed-effects model with system noise. Statistics and Probability Letters, 208, 110074 (2024). arXiv:2303.16639
  • Masuda, H., Mercuri, L., and Uehara, Y.: Quasi-Likelihood Analysis for Student-Lévy Regression. arXiv:2306.16790 (2023)
  • Kusano, S., Uchida, M.: Statistical inference in factor analysis for diffusion processes from discrete observations. Journal of Statistical Planning and Inference, 229 106095, (2024)
  • Kusano, S., Uchida, M.: Sparse inference of structural equation modeling with latent variables for diffusion processes. Japanese Journal of Statistics and Data Science, (Version of Record), (2023)
  • Tonaki, Y., Kaino, Y., Uchida, M.: Parameter estimation for linear parabolic SPDEs in two space dimensions based on high frequency data. Scandinavian Journal of Statistics, 50(4) 1568-1589 (2023)
  • Tonaki, Y. and Uchida, M.: Change point inference in ergodic diffusion processes based on high frequency data. Stochastic Processes and their Applications, 158, 1-39 (2023)
  • Kawai, T., Uchida, M.: Adaptive inference for small diffusion processes based on sampled data. Metrika, 86(2), 643-696 (2023)
  • Baba, T., Yoshida, N.: Log-rank test with coarsened exact matching. arXiv:2403.16121v2 (2024)
  • Tudor, Ciprian A., Yoshida, N.: Asymptotic expansion of the drift estimator for the fractional Ornstein-Uhlenbeck process. (2024)
  • Gloter, A., Yoshida, N.: Quasi-likelihood analysis for adaptive estimation of a degenerate diffusion process, arXiv:2402.15256 (2024)
  • Yoshida, J., Yoshida, N.: Penalized estimation for non-identifiable models. Annals of the Institute of Statistical Mathematics, to appear, arXiv:2301.09131 (2023) 
  • Yoshida, J., Yoshida, N.: Quasi-maximum likelihood estimation and penalized estimation under non-standard conditions. Annals of the Institute of Statistical Mathematics, SharedItarXiv:2211.13871 (2022)
  • Mishura, Y., Yamagishi, H.Yoshida, N.: Asymptotic expansion of an estimator for the Hurst coefficient. Statistical Inference for Stochastic Processes (2023), arXiv:2209.02919 (2022)
  • Tudor, Ciprian A., Yoshida, N.: High order asymptotic expansion for Wiener functionals. Stochastic Processes and their Applications, 164, 443-492 (2023)
  • Yamagishi, H., Yoshida, N.: Order estimate of functionals related to fractional Brownian motion. Stochastic Processes and their Applications, 161, 490-543 (2023)
  • Masuda, H.: Optimal stable Ornstein-Uhlenbeck regression, Japanese Journal of Statistics and Data Science, 6 (2023), 573-605. arXiv:2006.04630
  • Fujinaga, Y. and Masuda, H.: Mixed-effects location-scale model based on generalized hyperbolic distribution. Japanese Journal of Statistics and Data Science, 6 (2023), 669-704. arXiv:2209.14716
  • Eguchi, S. and Masuda, H.: Gaussian quasi-information criteria for ergodic Lévy driven SDE. Annals of the Institute of Statistical Mathematics, 76 (2024) 111-157.  arXiv:2203.04039

2022 (Apr 2022 - Mar 2023)

2021 (Oct 2022 - Mar 2022)

  • Uchida, M.: Statistical inference for stochastic differential equations from discrete observations. (in Japanese). Journal of the Japan Statistical Society, Japanese Issue. Volume 51, Issue 2, 245-273 (2022), DOI 
  • Yoshida, N.: Quasi-likelihood analysis and its applications. Statistical Inference for Stochastic Processes (2022), 23 February 2022, DOI
  • Kamatani, K., and Song, X.: Haar-Weave-Metropolis kernel. Bulletin of informatics and cybernetics, 54(1), 1-31 (2022), Accepted, Feb 20. arxiv:2111.06148, 2021
  • Taiji Suzuki, Atsushi Nitanda.:Deep learning is adaptive to intrinsic dimensionality of model smoothness in anisotropic Besov space. Advances in Neural Information Processing Systems 34 (NeurIPS2021), pp. 3609--3621, 2021. (Spotlight)
  • Atsushi Nitanda, Denny Wu, Taiji Suzuki.:Particle Dual Averaging: Optimization of Mean Field Neural Networks with Global Convergence Rate Analysis, Advances in Neural Information Processing Systems 34 (NeurIPS 2021), arXiv:2012.15477
  • Yue He, Reiichiro Kawai, Yasutaka Shimizu, Kazutoshi Yamazaki.:The Gerber-Shiu discounted penalty function: From practical perspectives, arXiv:2203.10680
  • Giulia Di Nunno, Yuliya Mishura and Kostiantyn Ralchenko: Volterra–Lévy and Volterra–Gaussian noises. In Sergei Silvestrov, Anatoliy Malyarenko, Ying Ni, Milica Rancic (Eds.), Stochastic Processes, Statistical Methods, and Engineering Mathematics — SPAS 2019, Västerås, Sweden, September 30–October 2 , Springer Proceedings in Mathematics & Statistics, Springer, Cham , pp. 261 - 304, - 2022
  • Remi Dhoyer and Ciprian A. Tudor.:Non Central Limit Theorem for the spatial average of the solution to the wave equation with Rosenblatt noise, Theory of Probability and Mathematical Statistics, (2022), Volume 106, Pages 105-119, DOI: 10.1090/tpms/1167
  • Mitsuki Kobayashi and Yasutaka Shimizu.:Least squares estimators based on the Adams method for stochastic differential equations with small Lévy noise, Japanese Journal of Statistics and Data Science, Accepted 19 March 2022
  • Yuliya Mishura, Sergiy Shklyar.:Gaussian Volterra processes with power-type kernels. Part I., Modern Stochastics: Theory and Applications, (2022), Pages 1-26, DOI 10.15559/22-VMSTA205
  • Denis Belomestny, Vytaute Pilipauskaite and Mark Podolskij.:Semiparametric estimation of McKean-Vlasov SDEs, Annales de l’Institut Henri Poincare,202200, Volume -, Issue -, Pages ---
  • Shohei Nakajima and Yasutaka Shimizu.:Asymptotic normality of least squares estimators to stochastic differential equations driven by fractional Brownian motions, Statistics and Probability Letters, (2022), Volume 187, DOI:10.1016/j.spl.2022.109476
  • Chihiro Watanabe, Taiji Suzuki.:AutoLL: Automatic Linear Layout of Graphs based on Deep Neural Network, IEEE Symposium Series on Computational Intelligence (SSCI 2021), (2021), Pages 1-10, doi: 10.1109/SSCI50451.2021.9659893.
  • Jevgenijs Ivanovs and Mark Podolskij.:Optimal estimation of some random quantities of a Levy process, Electronic Journal of Statistics ,202201, Volume 16, Issue 1, Pages 892-934
  • Chihiro Watanabe and Taiji Suzuki.:Deep two-way matrix reordering for relational data analysis, Neural Networks, (2022), Volume 146, Pages 303-315, doi: 10.1016/j.neunet.2021.11.028
  • Yuliya Mishura, Kostiantyn Ralchenko, Olena Dehtiar.:Parameter estimation in CKLS model by continuous observations, Statistics & Probability Letters,202202, Volume 184, doi: 10.1016/j.spl.2022.109391
  • Ehsan Azmoodeh, Yuliya Mishura, Farzad Sabzikar.:How Does Tempering Affect the Local and Global Properties of Fractional Brownian Motion?, Journal of Theoretical Probability,202203, Volume 35, Issue 1, Pages 484-527
  • Mizuo Nagayama, Toshimitsu Aritake, Hideitsu Hino, Takeshi Kanda, Takehiro Miyazaki, Masashi Yanagisawa, Shotaro Akaho, Noboru Murata.:Detecting cell assemblies by NMF-based clustering from calcium imaging data, Neural Networks,202205, Volume 149, Issue -, Pages 29-39

New developments in statistics for
stochastic systems toward data science
for large-scale spatiotemporal dependence

New developments in statistics for
stochastic systems toward data science
for large-scale spatiotemporal dependence

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