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An estimation variance, however, is not available for that estimator.
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Furthermore, we should be very cautious when assuming that estimators for Θ based on genetic diversity will inform us about whether recent adaptation produce hard or soft sweeps.
For this reason, an estimator that estimates the wheel rail adhesion condition and a traction controller which adjusts the wheel slip velocity to the optimal value should be added to the control scheme.
Theoretically, when W i =Z i, (Q_{i}^=0) and the above asymptotic variance formula shares the same formula as that for the estimator in Zhou and Wang (2000) as exactly expected.
In particular, we show that for the estimator to be useful a certain fraction of the population should be sampled, and this fraction depends on the total size of the population.
Then we prove that for the estimators βn and σn2 of the parameters β and σ2,n(βn∗−βn) and n(βn−β),n(σn2∗−σn2) and n(σn2−σ2) have the same limit distributions, respectively.
The price for this benefit is that the estimator is a biased estimator of the statistic in equation 1.1.
We can account for the belief that the estimator of m has a small variance by a choice of a prior distribution.
We show that our estimators for estimating the proportions (weights) of subpopulations are more efficient and robust than available estimators using a small simulation study.
(112) Thus Conjecture 1 means that, for estimators with variance ({v_{k} = b_{k}+C}), to sample the estimators using weights in equation (3) is better than using weights in equation (2).
Let be the positive-definite error covariance matrix for an estimator that achieves the CRLB.
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