Cramer-Rao Bound for Estimation After Model Selection and its Application to Sparse Vector Estimation

no code implementations • 15 Apr 2019 • Elad Meir, Tirza Routtenberg

Finally, we demonstrate in simulations that the proposed selective CRB is an informative lower bound on the performance of the maximum selected likelihood estimator for a general linear model with the generalized information criterion and for sparse vector estimation with one step thresholding.

Model Selection