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The most important estimator of GS is the MLE widehat{GS}=1-sumlimits_{k=1}^{K} hat{p}_{k}^{2}.
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The software allows the re-organization of the data previous to the analysis, the estimation of the most important estimators, and a battery of complementary statistical tests.
As mentioned before, this is due to the important estimation error exhibited by the K-factor estimator (11).
Most important, our estimator performance outperforms those of the existing ML techniques.
Thus, classical analytical tools for studying standard system properties like observability, which is very important in estimator design, cannot be directly applied.
Here, outlier detection methods in low and high dimension, as well as important robust estimators and methods for multivariate data are reviewed, and the most important references to the corresponding literature are provided.
We therefore believe that it is important to use estimators that depend on as few assumptions as possible, such as the ML estimator we have studied here.
We find that trimming individual observations with too much weight as well as the choice of tuning parameters are important for all estimators.
An important category of such estimators are known as model-assisted estimators (Särndal et al. 1992).
An important issue for the estimator design is the numerical conditioning of the equation system.
An important limitation for this estimator is inefficiency, because only data from uncensored/complete cases inform the final value.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com