Conferences

SMSP2021: Statistical Modeling for Stochastic Processes and related fields


Workshop on Statistical modeling for stochastic processes  and related fields (online) 

This workshop aims to exchange information on the state-of-the-art in statistical modeling for stochastic processes and related fields from theoretical, methodological, and implementation points of view.

Dates:
September 27 (Mon) - 30 (Thu), 2021

Time:
From 17:00 to 19:30 (in Japan Standard Time (JST))

Online workshop:
The style of the workshop is online.

Registration (free of charge):
Please click here to register. We will provide a zoom link to the e-mail address you will register.

Organizer:
Kengo Kamatani (ISM, Tokyo)
Hiroki Masuda (Kyushu University)
Nakahiro Yoshida (University of Tokyo)
Masayuki Uchida (Osaka University)

Sponsors:
It is supported by the Japan Science and Technology Agency CREST JPMJCR14D7.


Program (in Japan Standard Time) The world clock is available here.

The PDF version of the program is available here (containing the abstracts). The details of the satellite can be found here.


September 27 (Mon) Chair: Nakahiro Yoshida (University of Tokyo)

17:00 – 17:05 Welcome Address by Nakahiro Yoshida (University of Tokyo)

17:05 - 17:40 Nakahiro Yoshida (University of Tokyo)
Asymptotic expansion in volatility parametric estimation revisited

17:40 - 18:15 Yuliia Mishura (Taras Shevchenko National University of Kyiv)
High-Frequency Trading with Fractional Brownian Motion

18:20 - 18:55 Hiroki Masuda (Kyushu University)
On mixed-rates structure in Gaussian quasi-likelihood inference for Lévy SDE

18:55 - 19:30 Igor Cialenco (Illinois Institute of Technology)
A power variation approach to statistical analysis of discretely sampled semilinear SPDEs


September 28 (Tue) Chair: Hiroki Masuda (Kyushu University)

17:00 - 17:35 Maud Delattre (Universite Paris-Saclay, INRAE, MaIAGE)
Statistical inference for discretely observed stochastic differential equations with mixed effects

17:35 - 18:10 Lorenzo Mercuri (University of Milan)
yuima.PPR: New Developments for the Point Process in the YUIMA package

18:20 - 18:55 Alexei Kulik (Politechnika Wrocławska)
Approximation in law of Markov processes by non-linear regressions: analytic background and statistical applications

18:55 - 19:30 Ciprian Tudor (Université Lille 1)
Drift parameter estimation for the stochastic wave equation with space-time white noise


September 29 (Wed) [Satellite] Chair: Kengo Kamatani (ISM, Tokyo)

14:55 - 15:30 Goda Takashi (University of Tokyo)
Multilevel Monte Carlo methods for efficient nested simulations

15:30 - 16:05 Xin Tong (National University of Singapore)
Can Algorithms Collaborate? The Replica Exchange Method and Its Spectral Gap

16:05 - 16:40 Alexandre H. Thiery (National University of Singapore)
Exploiting geometry for walking larger steps in Bayesian Inverse Problems

September 29 (Wed) Chair: Kengo Kamatani (ISM, Tokyo)

17:00 - 17:35 Kengo Kamatani (ISM, Tokyo)
Scaling limit analysis of some piecewise deterministic Markov processes

17:35 - 18:10 Björn Sprungk (TU Bergakademie Freiberg)
Robust sampling methods for Bayesian inverse problems with small observational noise

18:20 - 18:55 Tony Lelievre (Ecole des Ponts ParisTech)
Adaptive importance sampling methods

18:55 - 19:30 Alexandros Beskos (University College London)
Manifold Markov chain Monte Carlo methods for Bayesian inference in diffusion models


September 30 (Thu) Chair: Masayuki Uchida (Osaka University)

17:00 - 17:35 Masayuki Uchida (Osaka University)
Adaptive test for ergodic diffusion processes from discrete observations

17:35 - 18:10 Mathias Vetter (Christian-Albrechts-Universität zu Kiel)
Jump regressions revisited

18:15 - 18:50 Markus Bibinger (Julius-Maximilians-Universität Würzburg)
Inference on jumps in high-frequency order-price models with one-sided noise

18:50 - 19:25 Yasutaka Shimizu (Waseda University)
M-estimation based on quasi-processes from discrete samples of Lévy processes

19:25 - 19:30 Closing Address by Masayuki Uchida (Osaka University)




Mathematical statistics and
stochastic analysis for modeling and
analysis of complex random systems

Mathematical statistics and
stochastic analysis for modeling and
analysis of complex random systems

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