Statistical Theory: Cramér-Rao Bound & Hypothesis Testing

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Description

This lecture covers the Cramér-Rao bound, asymptotic efficiency, and hypothesis testing in statistical theory. It explains the Fisher information, optimality in decision theory, and the Neyman-Pearson setup. The lecture delves into the asymptotic normality of the Maximum Likelihood Estimator (MLE) and the concept of point estimation for parametric families.

Official source

https://mediaspace.epfl.ch/media/0_oqveel3a

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