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There is one interesting difference between the method-of-moments estimates results of Gregory and Woolhouse [16] and the present results for ML estimation: the positive bias of k̂ mom was fairly constant as m increased (though the range of k̂ mom values was greatest for lower m), while the bias of ML estimates k̂ decreased for higher m (Fig. 1).
The difference of the cost estimates results from the vastly different design assumptions.
Merging the estimates results in the MLE τ ̂ MLE = 1 2 ∑ i ∈ { 0, 2 } τ ̂ MLE, i. (29).
From Figure 2, it is clear that error in trajectory estimates results in errors in estimated sensor positions.
Fourth, given the importance of publication bias in primary estimates, results from cumulative meta-analyses are used to correct summary statistics for this bias.
Feldpausch et al. [56],[57] found that an additional source of error in AGB estimates results from the exclusion of height as a predictor variable in allometric models.
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High correlated estimates result in ill-conditioned matrices degrading the behavior of this practical estimator.
The outcome is compared to estimates resulting from field-theoretic calculations.
All showed estimates result from two-sided testing.
Instrumental variables estimates resulted in comparable estimates against the "gold standard".
Finally, the estimates resulting from the structural model were pooled to obtain the final parameter estimates.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com