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In order to address the impact of perturbation on state estimation, an adaptive estimator based on modified mixture-of-expert framework is given.
We estimate this treatment effect using two propensity score matching estimators, a reweighting estimator based on the estimated propensity score and a genetic matching estimator.
The position is then estimated through the local linear estimator based on the built database.
We also compare the performance of the ANN-based estimator with the estimator based on maximum likelihood method (MLE).
The expected traffic load is accurately estimated using a sample mean estimator based on previously monitored traffic in each cell.
The degree of overfitting (shrinkage) in the model will be estimated using the heuristic shrinkage estimator (based on the log likelihood ratio χ statistic for the full model).
A frequently used estimator based on the average of estimated coancestries produced inflated coancestries and numerical instability.
Regarding variance estimation, Osier (2006, 2009) proposed a variance estimator based on linearization techniques.
In contrast, Yang et al. (2010) estimate relatedness using a method of moments estimator based on allele-sharing.
A variance estimator based on Schoenfeld residuals provided better variance estimates for severe model misspecification [ 32].
However, an estimator based on asymptotic expansions is proposed, overpassing the disadvantages of MLE-based methods that employ numerical or graphical techniques.
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