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While our treatment is Bayesian, we develop a LOD (log of odds) score estimator for assessing linkage from Gibbs sampling that is highly accurate for simulated data.
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Results of the model comparison show that at large spatial scales both models discriminate well between major tree distribution characteristics and can be considered as valid estimators for assessing regional vegetation patterns.
In addition, for assessing estimator of the nonparametric component g, we study the square root of mean-squared errors (RMSE) based on 1000 repetitions.
We now present computer simulation results for assessing the performance of the proposed channel estimator in selected system configurations.
Based on a general statistical framework for simultaneous inference and robust covariance estimators we propose a new statistical multiple comparison procedure for assessing multiple means.
Iterative extended KF and Gauss-Newton algorithms are developed for reduced-complexity tracking, along with accurate error covariance updates for assessing performance of the resultant sparsity-aware state estimators.
Accuracy (absence of bias) and precision (prediction intervals) of the variance component estimators were assessed for each simulated sampling strategy.
The performance of the proposed joint AML estimator is assessed via computer simulations and compared with that achieved by the joint AML estimator designed for AWGN channel and that achieved by a previously derived joint estimator for OFDM systems.
These estimators, assessing the mean function u x, have seen extensive application and assessment in the cost data literature, but not necessarily for length of stay.
The performance of the joint AML1 estimator for multipath channel has been assessed via computer simulations and compared with that achieved by the joint AML2 estimator designed for AWGN channel.
In Section 5, the performance of the joint ML estimators is assessed with numerical results in multipath and noise channels for different signal bandwidths.
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