Evaluation of model ability to predict the experimental data was based on both regression correlation coefficient values (r) and difference between experimental (q t,exp) and model-estimated (q t,est) values, evaluated by means of the error measure: (4) RMS = 100 ∑ [ (q t, est − q t, exp ) / q t, exp ] 2 N, where N is the number of experimental points in each q t versus t curve.
The performance of image registration methods can be measured by the centerline error measure [18, 26].
The first column shows time step sizes, the second does the errors measured by sup norm at the final time, and the last gives the rates between the errors generated by using the previous and the current step sizes.
end{aligned} The above estimations suggest that if we apply the wavelet of order p and (uin H_{0}^{1}(varOmega ) cap H^{s} varOmega )), then the errors measured by the standard norm, and the Sobolev norm in (L^{2}), decrease by the factors of (2^{p+1}), and (2^{p}), respectively, from n to (n+1) level.
Compared to raw average β, we observed each of the normalizations reduced the errors measured by average absolute deviation from zero.
We can assess the abundance estimates produced by the methods by using error measures such as the relative root mean square error, RRMSE and the average relative error, AVGRE (Supplementary Methods for definition).
A suitable smoothing factor can be found by minimising an error measure, like the sum of square errors (SSE), on representative sample data.
Robust regression replaces the squared error by other error measures reducing the influence of outliers [ 51].
We analyze this detector by extending the positive FDR (pFDR) error measure introduced by Storey to characterize the errors encountered in performing multiple, independent and nonidentical hypothesis tests simultaneously[48].
Standard errors in the θ tip angles were calculated by propagating the error in measuring the positions of each tip of the tube.
