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The proposed neural network model showed a better performance than regression analysis regarding the maximum estimation error and root mean square error.
Categories and ratings of six parameters in the probability-based DRASTIC model were probabilistically characterized according to the parameter classification methods of selecting a maximum estimation probability and calculating an expected value.
There are two methods commonly used in the statistics and social network communities to estimate the maximum likelihood fit to ERG models: Markov chain Monte Carlo maximum likelihood estimation and maximum pseudo-likelihood estimation.
The parameter estimation methods we are applying are least squares estimation, maximum likelihood estimation, and maximum likelihood with jackknife estimation.
Equation 4 was estimated using maximum likelihood estimation and the estimated results are summarized in Table 3.
The maximum estimation errors of the KF- and JKF-based estimators are less than 1.8 °C and 1 °C respectively.
In consequence we can only give a minimum and maximum estimation of the response rate.
Parcel scores were estimated with the WLE method, which is less biased than maximum likelihood estimation, and provides best point estimates of individual ability (Warm 1989).
All analyses were performed using SAS® PROC MIXED [ 7] with Maximum Likelihood estimation, and tests of parameter estimates were performed using likelihood ratio tests.
For the generalised BRMA model, SAS NLMIXED currently only allows maximum likelihood estimation and so extension to REML is required, especially as the maximum likelihood estimates are not without bias (Table 5).
CarthaGene looks for multiple populations' maximum likelihood consensus maps using a fast EM algorithm for maximum likelihood estimation and powerful ordering algorithms.
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