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These results provide evidence of the strengths and weakness from both the CRBs and the practical estimator at hand.
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Afterwards, the resulting solution is converted back to the original coordinate space, yielding a globally stable time varying optimal estimator for the problem at hand.
We certainly do not suggest that the maximum likelihood estimator should be used if the data at hand are better described by other models (such as Models M0 or Mt, for example).
Second, the analysis used models and estimators suitable to the polychotomous characteristics of the data at hand (polychoric transformations and probit models via WLSMV estimator).
Rather, as we suggest in our paper, application of several estimators, relying on different assumptions, might provide complementary information about the data at hand and might help in getting more reliable estimates.
As the performance of an estimator depends on the defining objective function, the properties of the objective function at hand are analyzed in the following.
Help is at hand.
Armageddon was at hand.
Solutions are at hand.
Then help is at hand.
Artificial evolution is at hand.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com