Sentence examples for comparing root mean square from inspiring English sources

Model fit was evaluated by comparing Root Mean Square Error of Approximation (RMSEA), Comparative Fit Index (CFI) and Tucker-Lewis fit Index (TLI) from the two different models.

By comparing root mean square error (RMSE) and mean bias error (MBE), dynamic artificial neural network model with sigmoid activity function and 17 neurons in the hidden layer was chosen as the best model for forecasting inflow of the Dez dam reservoir.

For yeast, we started with 4 different initial conditions, conducted 5 trials each, and grew 20 replicas for each parameter set to obtain an average distribution, which we score by comparing the root mean square difference of the binned distribution to the binned yeast distribution.

Non-linear behavior and sensitivity of the new feature are compared with root mean square (RMS) peak value and peak position.

Such shortened track segments would make it difficult to compare the root mean square (RMS) residuals of neighboring tracks (see below).

We could then compare the root mean square error (RMSE) between the experimental threshold distance and the threshold distance predicted by the various models.

At the individual level, individual true and estimated admixture values were compared, and root mean square error (RMSE) was used as a summary measure of precision in the estimation of individual ancestry proportion.

The performance of several estimation methods were compared by root mean square error (RMSE); this is a relevant measure for studying the performance of biased estimators, as it combines the systematic bias and the random error of an estimator.

To verify the network unfolding performance, the original and unfolded spectra were compared using the root mean square error.

Finally, both modeling methodologies were statistically compared by the root mean square error and absolute average deviation based on the validation data set.

The performance of the 27 proposed models along with the empirical Stephens and Stewart (SS) and physically-based PenPan models were investigated and compared using relative root mean square error (RRMSE), Nash-Sutcliffe coefficient (NS) and mean absolute error (MAE).