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An Introduction to Information Theoretic Minimax Lower Bounds

Jennifer Rogers, University of Washington
Thursday, April 11, 2019 - 11:00am to 12:00pm
PDL C-401
When designing statistical estimators, we want to know how far our estimator is from the theoretical "best." Lower bounds help us answer this question by providing a guarantee of the form "no estimator that uses {some amount of information} to estimate {your quantity of interest} can have error lower than {some data-dependent quantity}." In this talk, we will describe Le Cam's method for proving minimax lower bounds on the risk of an estimator, using a reduction to two hypotheses. No prior knowledge of information theory or minimax lower bounds will be assumed.
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