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In all the three models analysed, the omnibus test's p-value was < 0.001 indicating that the proposed models outperforms the null model (with no predictors).

Our results demonstrate that the analysis of dual-color microarray gene expression experiments using intensity-based linear models outperforms the standard ratio-based analysis.

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The correlated random effect models outperformed the others.

The FLR models outperformed the MLR ones (p > 0.01).

The traditional models outperform the linear model when the standard deviation is small.

Moreover, our 5d QM QSPR models outperform the models from Svobodova and Geidl.

The PCM models outperform the QSAR models (on all datasets and with all descriptors).

The results reveal that the proposed models outperformed the standardized MGGP models.

Results showed that our two models outperformed the previous spatial GLM and the hot spot model.

In general, the two ANN-based models outperformed the GP model in terms of several indices.

The DTF and DTB models outperformed the SVM both in classification and regression.

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