School of Mathematical Sciences
Monday, May 16, 2016
Holcblat hall 007, 12:15
Universite Pierre et Marie Curie
Universal estimators - maximum likelihood and beyond
Abstract: One major problem in Statistics is (in its simplest form) the following: given n i.i.d. random variables X1, . . . , Xn with a common unknown distribution P and the assumption that P belongs to some given family 𝒫 of probabilities, use the Xi to guess P ∈ 𝒫 .
20ís, Ronald Fisher invented and popularized the Maximum Likelihood
method which has been considered for a long time and by many people as
some sort of a universal estimator. Its properties have been studied at length but it was also discovered that it suffered from various weaknesses.
explain the method and show why it may or may not work, point out some
major deficiencies, then explain how to replace it by an alternative
solution, based on notions related to metric entropy and robustness,
that substantially improves on the Maximum Likelihood, at least from a
theoretical point of view.
Coffee will be served at 12:00 before the lecture
near Holcblat hall 007