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This estimator enabled optimizing the number of probes/markers to be used per class.
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Having access to the bias of the estimator, enables us to evaluate the accuracy of the estimated clean data, which later will be used to design a quantizer.
The estimator enables significant reduction in the computation time required to perform complex word-length optimizations.
However, our simplified expression for the risk estimator enables us to compute the aggregated risk without any bootstrapping.
The error estimator enables an a posteriori improvement of GI, GII for a given finite element mesh.
Our extended CRE estimator enables us to quantify the quality of a given probabilistic model and, thus, to update and validate the model.
This novel so-called partially reflecting random walk on spheres estimator enables Monte Carlo methods to compute the voltage-to-current map in an embarrassingly parallel manner, which is an important issue with regard to the corresponding inverse problem.
Parameter estimators enable the on-line and direct reconstruction in finite time of harmonic components and deviations of some measurable signal.
Thus as in the unweighted case, WM-GC estimators subsume GGD-based (weighted) estimators, indicating that WM-GC estimators are at least as powerful as GGD-based estimators (linear FIR, weighted median, weighted FLOM) in light-tailed environments, while WM-GC estimator characteristics enable them to substantially outperform in heavy-tailed impulsive environments.
The study applies a matching estimator that enables control for both the composition of unemployed and activation types and finds that the long-run effect of activation is positive in terms of improving employment rates for the unemployed.
The study uses Swedish data from 1999 to 2005, and like Lechner & Wunch (2009>) they use a matching estimator that enables control for both the composition of unemployed and activation types.
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CEO of Professional Science Editing for Scientists @ prosciediting.com