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The aim of this paper is to evaluate the behavior of each method on treatment effect on real data of a RCT and on simulated data.
To further illuminate the behavior of each method, we consider a large set of sampling instances and present the averaged recovery performance as a function of the sampling rate in Fig. 4 for Intel-Berkeley (top) and SensorScope (bottom) data.
In the following, we explain the behavior of each method in common terms.
To analyze the typical behavior of each method, we measured the average prediction error and the average relative parameter error (Table 1 and Supplementary Fig. S10).
Here, our intention is not to actually predict the transcript abundance data, but to evaluate the behavior of each method under optimal conditions.
To analyze the typical behavior of each method, we measured the average prediction error and the average relative parameter error of the best parameter solution (Table 2 and Supplementary Fig. S12).
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It was a sort of an 'acid test,' since our aim was to observe the behavior of each of the methods in the analysis of a waveform with a very wide spectrum, i.e., exceeding a frequency of 20 kHz.
Also, this case study was a sort of 'acid test,' since our aim was to observe the behavior of each of the methods in the analysis of a waveform with a spectrum that exceeded a frequency of 2 kHz and that had embedded successive harmonic and interharmonic components.
The accuracy, grid independence, convergence behavior, and computational efficiency of each method are critically examined.
As illustrated above, the behavior of a method can depend upon the changing attributes of the object.
Numerical examples illustrating the behavior of the method are presented.
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