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The logistic procedure fits linear logistic regression models for binary or ordinal response data using Maximum Likelihood estimations and compares the estimated samples whereas artificial neural network systems attempts to assign proper weights to the respective inputs by a 'genetic algorithm' optimization procedure to allow for the correct deduction of the ultimate outcome.
Then, combining with the acceleration models, the maximum likelihood estimations (MLE) of the model parameters are obtained.
The simulation results show that the maximum likelihood estimations of the mean life and the failure rate at the design stress have positive bias, and the unbiased estimations of the mean life and the failure rate at the design stress have smaller mean squared errors than the maximum likelihood estimations of these parameters in small and moderate samples.
Models 2 through 4 are full models using maximum likelihood estimations (MLE) with unstructured covariance structure.
The results of the maximum likelihood estimations are reported in Table 5.
Interested in reconstructing phylogenetic trees via distance based methods and/or Maximum Likelihood Estimations, applying algebraic techniques to contingency table problems, and lattice points in rational convex polyhedra.
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It is estimated using maximum likelihood estimation technique.
The OLR model was estimated using maximum likelihood estimation method.
Subsequently, the parameters were estimated by maximum likelihood estimation.
The regression coefficients were estimated using maximum likelihood estimation.
The model will be estimated using maximum likelihood estimation.
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