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To validate the accuracy of our estimation, we have also estimated confidence intervals using the bootstrap method [16, 17].
We now present experimental results on the accuracy of our estimation of the expected vector after a given number of random DCJ operations and on the quality of our estimator for the true evolutionary distance (in terms of the actual number of DCJ operations).
We also explain how the proposed approach can be extended to a general system of N agents and discuss efficient computation of our estimation strategy.
A comparison of our estimation results to other rock properties suggests that, at the lab-scale, the geometric mean of Dp increases with bulk porosity and the quantity of macroscopic features such as vugs and fractures.
Proof of Theorem 4 We first discuss the consistency of our estimation.
A main limitation of our estimation is the cross-sectional design.
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This suggests high precision of our estimations.
This value belongs to the accuracy range of our estimations of the nonlinear optical coefficients.
In all of our estimations, we employ micro-level labor market data for natives.
In most of our estimations, we find a significantly positive relationship between the net replacement rate and the wage.
This might explain why some of our estimations did not reach statistical significance.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com