Talks

2024 (Apr 2024 - Mar 2025)

Uchida, M: Estimation for a discretely observed linear parabolic SPDE in two space dimensions based on triple increments. EcoSta 2024, Jul 17, 2024
Uchida, M: Statistical parametric estimation for a linear parabolic SPDE in two space dimensions based on temporal and spatial increments. Dynstoch 2024, May 24, 2024
Yoshida, N.: Asymptotic expansions for functionals of a fractional Brownian motion. 30 ans du Laboratoire Manceau de Mathématiques : Probabilités - Statistique - Risque, Le Mans, May 21, 2024

2023 (Apr 2023 - Mar 2024)

Uchida, M: Parameter estimation for discretely observed linear parabolic SPDEs in two space dimensions with small noises. IMS-APRM 2024, Jan 6, 2024
Uchida, M: Estimation for a linear parabolic SPDE in two space dimensions with a small noise based on high-frequency data. CMStatistics 2023, Dec 17, 2023
Uchida, M: Parametric estimation for discretely observed linear parabolic SPDEs in two space dimensions. EcoSta 2023, Aug 1, 2023
Uchida, M: Estimation for a linear parabolic SPDE in two space dimensions from discrete observations. 64th ISI World Statistics Congress 2023, Jul 17, 2023
Yoshida, N.: Asymptotic expansion for batched bandits. IMS-Asia-Pacific Rim Meeting 2024, Melbourne, Australia, January 4-7, 2024.
Yoshida, N.: Asymptotic expansion for general Wiener functionals. 17th International Conference Computational and Financial Econometrics (CFE 2023), HTW Berlin, University of Applied Sciences, Berlin, Germany, December 16-18, 2023.
Yoshida, N.: Malliavin calculus and precise distributional approximations. Workshop on Eco-Stat Asymptotics 2023 (WESA2023), University of Verona, Verona, September 11, 2023
Yoshida, N.: Quasi-likelihood analysis and estimation for a degenerate diffusion process. 6th International Conference on Econometrics and Statistics (EcoSta 2023), Waseda, Tokyo, August 1-3, 2023.
Yoshida, N.: Higher-order asymptotic distribution theory with the Malliavin calculus and its applications to statistics. 64th ISI World Statistics Congress, IPS 150 - Statistical Inference For Stochastic Ordinary And Partial Differential Equations, Ottawa, Canada, July 16-20, 2023.
Yoshida, N.: Some recent developments in asymptotic expansion. Mathematical Finance and Stochastics: A Conference in Honor of David Nualart, San Sebastian, Spain, May 29-31, 2023.
Masuda, H.: Locally stable regression (May 19, 2023; Colloquium, University of Tokyo)
Masuda, H.: Consistent model selection for locally stable trend-scale regression (June 6, 2023; EFFI Japan-French statistics seminar, online)
Masuda, H.: Expanding quasi-likelihood inference for Lévy driven models (July 20; 64th ISI Statistics Congress - Ottawa, Canada)
Masuda, H.: Non-Gaussian Ornstein-Uhlenbeck regression (July 28; The 4th International Conference on Science, Mathematics, Environment and Education (ICoSMEE), virtual)
Uehara, Y.: Quasi-likelihood analysis for Student-Levy regression (August 1; 6th International Conference on Econometrics and Statistics)
Masuda, H.: Asymptotics for Student-Lévy regression (August 25; ICIAM 2023, Waseda University, Japan)
Masuda, H.: Asymptotic inference for a non-Gaussian location-scale mixed-effects model (September 22; MSJ Autumn Meeting 2023 at Tohoku University)
Masuda, H.: Robustifying Gaussian quasi-likelihood inference for volatility (November 14, 2023; Workshop "New Developments in Statistical Theory and Methodology in Data Science", Kyushu University)
Masuda, H.: Robustifying Gaussian quasi-likelihood inference (December 17, 2023; CMStatistics, HTW Berlin, University of Applied Sciences)
Eguchi, S.: Robustifying Gaussian quasi-likelihood inference in YUIMA (December 18, 2023; CMStatistics, HTW Berlin, University of Applied Sciences)
Masuda, H.: Asymptotics and computation of robust Gaussian quasi-likelihood inference (January 6, 2024; IMS-APRM 2024, The University of Melbourne’s Parkville Campus)
Masuda, H.: Asymptotics for a dynamic mixed-effects model with low-frequency and unbalanced data (February 6, 2024; Stochastic Analysis and Statistics 2024, University of Tokyo)
Masuda, H.: Student-Lévy regression with high-frequency sampling (February 14, 2024; Workshop "Infinitely divisible processes and related topics", ISM)

2022 (Apr 2022 - Mar 2023)

Masuda, H.: Formulae for comparing ergodic SDE models (June 30, 2022; Dynstoch meeting 2022, Paris, online)
Masuda, H.:Continuous non-Gaussian mixed-effects modeling (September 5, 2022; Japanese Joint Statistical Meeting 2022, Seikei University, Japan)
Masuda, H.:BIC-type model selection for locally stable regression (September 16, 2022; MSJ Autumn Meeting 2022, Hokkaido University, Japan)
Masuda, H.:On noise inference for ergodic Lévy driven SDE (December 8, 2022; Workshop: Infinitely divisible processes and related topics, ISM, Online)
Masuda, H.:Quasi-likelihood inference for Student-Lévy regression (December 18, 2022; CMStatistics, King's College London)
Masuda, H.:Mixed-effects location-scale model based on generalized hyperbolic distribution (March 7, 2023; CREST workshop, ISM, hybrid)
Yoshida, N.: Batched bandits and conditional Edgeworth expansion. DYNSTOCH 2023 - Workshop on Statistical Methods for Dynamical Stochastic Models, Imperial College London, March 28
Yoshida, N.: Partial mixing and asymptotic expansion for batched bandits. CMStatistics 2022 (online). King's College London, UK, December 19, 2022.
Uchida, M: Estimation for a discretely observed linear parabolic SPDE in two space dimensions with a small noise. Dynstoch 2023, Mar 28, 2023
Uchida, M: Estimation for linear parabolic SPDEs in two space dimensions based on high-frequency data. CMStatistics 2022 (online). King's College London, December 18, 2022.
Yoshida, N.: Adaptive and non-adaptive estimation for degenerate diffusion processes. Statistics of Stochastic Processes in Discrete and Continuous Time (on-line). Kyiv, Ukraine, October 11, 2022
Masuda, H.: BIC-type model selection for locally stable regression (September 16, 2022; MSJ Autumn Meeting 2022, Hokkaido University, Japan)
Uchida, M: Parameter estimation for a linear parabolic SPDE in two space dimensions with a small noise from discrete observations. Statistics for Stochastic Processes: SDEs, SPDEs and concentration of measure (online). University of Luxembourg, September 7, 2022
Masuda, H.: Continuous non-Gaussian mixed-effects modeling (September 5, 2022; Japanese Joint Statistical Meeting 2022, Seikei University, Japan)
Uchida, M: Parameter estimation for linear parabolic SPDEs in two space dimensions from discrete observations. Dynstoch meeting 2022 (online), Institut Henri Poincaré, Paris, July 1, 2022
Masuda, H.: Formulae for comparing ergodic SDE models (June 30, 2022; Dynstoch meeting 2022, Paris, online)
Yoshida, N.: Simplified quasi-likelihood analysis (on-line). DYNSTOCH 2022, Institut Henri Poincaré, Paris, France, June 30, 2022
Yoshida, N.: Asymptotic expansion in volatility parametric estimation revisited. Scale Invariance and Randomness, June 8, 2022, Laboratoire Paul Painlevé, Université de Lille, France
Kamatani, K.: Non-Reversible Guided Metropolis Kernel. SIAM Conference on Uncertainty Quantification, Atlanta, Georgia, United States (Hybrid), April 12, 2022 (joint work with X. Song)
Yoshida, N.: Asymptotic expansion of variations. Seminar - ANR EFFI, April 5, 2022, France, on-line
Eguchi, S.: Model comparison for ergodic SDEs in YUIMA. 15th International Conference of the ERCIM WG on Computational and Methodological Statistics (CMStatistics 2022), King's College London(host), UK, December 17-19, 2022.

2021 (Oct 2021 - Mar 2022)

Masuda, H.:Estimation and selection of ergodic Lévy driven SDE: an overview of some recent developments. MFO-RIMS Tandem Workshop "Nonlocality in Analysis, Probability and Statistics" (Online (Hybrid)), March 22, 2022.
Masuda, H.:Information criteria for ergodic Lévy driven SDE, Workshop: Infinitely divisible processes and related topics" (ISM, Online), November 25, 2021 (joint with S. Eguchi).
Kamatani, K.: Scaling limit of Markov chain/process Monte Carlo methods. IASC-ARS 2022, Kyoto, Japan (Hybrid), Febrary 22, 2022 (joint work with J. Bierkens and G. O. Roberts), Keynote Talk