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The first is the standard maximum likelihood criterion based solely on least-squares model error.
Finally, the model parameters of GMM were estimated under the maximum likelihood criterion by using the standard expectation-maximization algorithm.
The models are estimated by maximum likelihood, the standard errors are robust and clustered at the level of the department and all models include dummy variables for the department of the respondent.
stand for maximum likelihood and standard error, respectively.
Note that, under the settings chosen for MLTreeMap, the actual likelihood computations in RAxML follow the standard Maximum Likelihood approach under a standard protein evolution model, for maximum accuracy.
16 In practice we use a partial maximum likelihood estimator, clustering standard errors at the individual level.
In lieu of a more common panel data estimator, we opt for a multi-level generalized linear model (GLM) approach estimated with Stata/IC 14's xtmixed command using maximum likelihood with clustered standard errors.
The default estimator was maximum likelihood with robust standard errors.
Model estimation was done with Maximum Likelihood with robust standard errors (MLR).
Maximum likelihood robust (MLR) standard errors was used as the estimation method for all analyses.
The trajectories were estimated using maximum likelihood with robust standard errors (MLR), which is robust regarding non-normality of the scores.
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