Kutoyants, Y.

Volatility estimation of hidden Markov processes and adaptive filtration, Stochastic Processes and their Applications, 173

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
- Time series
- Fractional processes

- All years
- 2024
- 2023
- 2022

Kutoyants, Y.

Volatility estimation of hidden Markov processes and adaptive filtration, Stochastic Processes and their Applications, 173

Dombry, C. and Esstafa, Y.

The vanishing learning rate asymptotic for linear $L^2$-boosting, ESAIM PS, forthcoming.

Ben-Hariz, S., Brouste, A., Cai, C. and Soltane, M.

Fast and asymptotically efficient estimation in an autoregressive process with fractional type noise, Statistical Planning and Inference, forthcoming

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

One-step closed form estimator for generalized linear model with categorical explanatory variables, Statistics and Computing, 33(138) (2023)

Badreau, M. and Proia, F.

Consistency and asymptotic normality in a class of nearly unstable processes, Statistical Inference for Stochastic Processes*,* 26, 619–641 (2023).

Chigansky, P. and Kleptsyna, M.

Estimation of the Hurst parameter from continuous noisy data, Electronic Journal of Statistics, 17(2), 2343-2385 (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, 27, 156-173 (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