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The purpose of this statistical technique is to estimate the distribution of the estimator around the unknown true value θ.
The use of the estimated coefficient of variation (COV) of the estimator is discussed.
The main requirement for CI estimation is to know the sampling distribution of the estimator in question [15].
Shrinkage covariance estimation compromises between the unbiasedness and small variance of the estimator and outperforms its previous methods.
In order to understand the properties of the estimator I, we must first define the quantity that is being estimated.
The quality of the estimator is tested.
The new methods reduce mainly the bias of the estimator.
The performance of the estimator is demonstrated by simulation results.
However, that might lead to instability of the estimator.
Finally, an efficient implementation of the estimator is designed.
Various cases for the design of the estimator are presented.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com