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About the van Trees inequality and its use for statistical lower bounds.
1 : Laboratoire de Mathématiques d'Orsay
Université Paris-Saclay, Centre National de la Recherche Scientifique, Centre National de la Recherche Scientifique : UMR8628
In this talk, I will present the van Trees inequality and how it can be used to derive lower bounds on the minimax quadratic risk for parametric, semi parametric and non parametric problems. In particular, I will provide an elementary proof of the local asymptotic minimax theorem for quadratic loss functions, avoiding the beautiful but sophisticated Hajek-Le Cam theory of convergence of experiments.
