Masuda, H., Mercuri, L. and Uehara, Y. .

Noise inference for ergodic Lévy driven SDE, Electronic Journal of Statistics, 16(1), 2432–2474 (2022).

Efficient
inference
for large and
high-frequency
data

The statistical decision theory deals with the problem of **constructing an optimal decision for a given statistical experiment**.

The statistical decision theory deals with the problem of **constructing an optimal decision for a given statistical experiment**.

The theory of statistical experiments deals with the **convergence of statistical experiments**
and the construction of **asymptotical optimal decision**.

The theory of statistical experiments deals with the **convergence of statistical experiments**

and the construction of **asymptotical optimal decision**.

Since the convergence (in a reasonable sense) on the initial sequence of statistical experiments cannot be expected…

Since the convergence (in a reasonable sense)

on the initial sequence of statistical experiments cannot be expected…

…**localized statistical experiments are built**
(mimicking the centering and renormalization in the central limit theorem).

…**localized statistical experiments are built**

(mimicking the centering and renormalization in the central limit theorem).

The convergence of the sequence of localized statistical experiment to a “simple” canonical experiment for which the optimal decision can be defined…

The convergence of the sequence of localized statistical experiment

to a “simple” canonical experiment for which the optimal decision can be defined…

…allows to define **the optimal decision in the localized statistical experiments**
for sufficiently large size of the sample.

…allows to define **the optimal decision in the localized statistical experiments**

for sufficiently large size of the sample.

Moreover a **“global” optimal decision** can be built
in the initial corresponding statistical experiment.

Moreover a **“global” optimal decision** can be built

in the initial corresponding statistical experiment.

The project aims to **improve the knowledge on asymptotic efficiency**
and to provide **new and innovative efficient estimators** and testing procedure
for large and high-frequency dataset encountered in real applications.

The project aims to **improve the knowledge on asymptotic efficiency**

and to provide **new and innovative efficient estimators** and testing procedure

for large and high-frequency dataset encountered in real applications.

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2022

SeminarANR EFFI France-Japan seminarApril 5, 2022 -

Le Mans Université / University of Tokyo / Online

2023

SeminarANR EFFI Japan-France seminarJune 6, 2023 -

Le Mans Université / University of Tokyo / Online

2024

- OneStep
- Yuima

- All tasks
- Stochastic differential equations
- Fractional processes
- Times series

- All years
- 2022
- 2023

Masuda, H., Mercuri, L. and Uehara, Y. .

Noise inference for ergodic Lévy driven SDE, Electronic Journal of Statistics, 16(1), 2432–2474 (2022).

Afterman, D., Chigansky, P., Kleptsyna, M. and Marushkevych, D.

Linear filtering with fractional noises : large time and small noise asymptotics,

SIAM J, Control and Optimisation, 60(3), 1463-1487 (2022)

Brouste, A., Dutang, C. and Rohmer, T.

A closed-form alternative estimator for GLM with categorical explanatory variables, Communications in Statistics – Simulation and Computation (2022)

Bayer, C., Fukasawa, M. and Nakahara, S.

On the weak convergence rate in the discretization of rough volatility models,

SIAM J. Finan. Math., 13, 66-73 (2022)

Chernoyarov, O., Dabye, A., Diop, F., Kutoyants, Y.

Non asymptotic expansions of the MME in the case of Poisson observations,

Metrika, 85, 927-950 (2022).

Ben-Hariz, S., Brouste, A., Esstafa, Y. and Soltane M.

*Fast calibration of weak FARIMA models*, ESAIM PS (2023)

Brouste, A. and Farinetto, C.

*Fast and efficient estimation in the Hawkes processes, Journal of Japanese Statistics and Data Science (2023)*

Contact Alexandre Brouste Scientific Coordinator alexandre.brouste@univ-lemans.fr