A K-components Gaussian mixture model with equal variances was chosen to modelize the marginal distribution of the transformed test statistics, as such a mixture model efficiently separates the empirical null distribution (likely to be composite and different from the theoretical one [28], [29]) from the alternative distribution.
In this paper, the interference model with equal left- and right-neighbor effects is considered.
Two transport models, with equal diffusivity and mixture-average diffusivity assumptions respectively, are considered.
The model was first proposed as a mixture Rasch model with equal item discrimination parameters for all items.
Analyses were performed with DFREML using a model with equal design and herd year season of first parity as a fixed effect.
Theoretical and numerical results demonstrate that the relaxed Lasso produces sparser models with equal or lower prediction loss than the regular Lasso estimator for high-dimensional data.
According to the fit indices, the model with equal intercepts across the three countries (model 7) is missing the cut off criteria provided by Rutkowski and Svetina (2014).
Also, because of the varying estimation samples it is difficult to present nested models with equal number of observations for each estimated specification when exploiting multiply imputed data.
A model with equal weights on each objective resulted in a decrease of 3% in average inbreeding but also reduced average Net Merit by $170 from the single-objective optima.
Under this framework, two optimization strategies are compared for each model with equal number of fitness evaluations: (1) when considering a random initial population and (2) when including the best single-objective optimal design in the initial population.
The results show that for all data sets RRegrs reports models with equal or better performance for both training and test sets than those reported in the original publications.
