This paper is organized as follows: Classical end-performance estimator designs within the class of unbiased estimators are discussed in Section 2 and ...
Jul 27, 2015 · These systematic approaches aim at introducing MSE bounds that are lower than the unbiased Cramer-Rao bound (CRB) for all values of the unknown ...
PDF | It is well known that appropriately biasing an estimator can potentially lead to a lower mean square error (MSE) than the achievable MSE within.
Jul 26, 2015 · In this context, the distance of the estimated system from the true one is measured in terms of a particular end-performance metric. This ...
These systematic approaches aim at introducing MSE bounds that are lower than the unbiased Cramér-Rao bound (CRB) for all values of the unknown parameters and ...
This survey introduces MSE bounds that are lower than the unbiased Cramer–Rao bound for all values of the unknowns and presents a general framework for ...
On the end-performance metric estimator selection (2015) | Dimitrios ...
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TL;DR: This work solves a constrained optimization problem where the additional linear constraints are imposed in the form of partially known boundary ...
These systematic approaches aim at introducing MSE bounds that are lower than the unbiased Cramér-Rao bound (CRB) for all values of the unknown parameters and ...
Are there endperformance metrics for which the ML estimator outperforms the MMSE when the experiment is finite-length? Recently, a framework for ...
and they will reveal the dependency of the system estimator's selection on the considered (any) end- performance metric. Remarks: 1) A useful, alternative ...