STATS 203 - Large Sample Theory (Spring 2025) Lecture 16: Efficiency
JSB UCLA · 45:49
The lecture argues that, under regularity, the MLE (and other first-order equivalents) is asymptotically efficient with variance \(1/I(\theta)\), but “best estimator” is not uniquely defined: Hodges-type improvements...