Using model-based estimation positional errors affect the model parameter estimates and thus the estimators.
Based on these observations, we hypothesize that the objective function manifold that uses a combination of RMSE and constraints gets rid of various local minima that correspond to physically meaningless parameter estimates and thus facilitates a much more efficient estimation of parameter values.
However, as suggested by the results in Table 1, a major problem in pan-genome size estimation is the fact that the data themselves are estimates, and thus the uncertainty in the computation of gene families will influence the results, sometimes severely.
A potential estimation error is likely to non-differentially affect both MAF estimates, and thus, is expected not to bias incidence estimates.
Parameter estimates and, thus, stream depletion predictions varied over a wide range.
This can be achieved by performing experiments designed to increase the accuracy of the parameter estimates and, thus, the model predictions.
The computational effort of each subsystem depends only on the chosen degree of overlap in the estimates and thus stays constant with an increasing number of subsystems, which makes the approach convenient for the operation of large systems.
For decisions that incur this kind of risk, historical experience serves well as the basis for a priori probability estimates and thus provides an invaluable guide to the future.
The results are almost identical to the fixed effects model estimates and thus are not reported in this paper.
Lemma 4.1 can be proved by employing the standard energy estimates and thus we omit the details for brevity.
It is also shown that the iterative receiver gives better Doppler estimates, and thus, improving the interpolation and phase tracking.
