ANR EFFI

ANR EFFI
Efficient inference for large and high-frequency data

The theory of Local Asymptotic Mixed Normality (LAMN) provides a powerful framework under which the asymptotic optimality of estimators and tests of hypothesis for finite-dimensional parameters can be studied. When the LAMN property holds true for a statistical experiment with a non-singular Fisher information matrix, minimax theorems can be applied and a lower bound for the variance of the estimators can be derived. Moreover, the asymptotic power of a test of hypothesis can be evaluated by a computation under the null hypothesis.

The project aims to improve the knowledge on efficiency for several statistical experiments based on various stochastic processes (particularly for singular high-frequency statistical experiments) and to provide new and innovative efficient estimators and testing procedure for real applications in insurance and finance.

Website: www.effi-stats.fr