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Changing the a priori data error does not influence shear wave velocity models significantly.
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As an example, with stationary data the local error does not decrease monotonically over time.
However, when experimental values of radiance data are corrected for backscatter, the error doesn't exceed the value of a random error in D (i.e., 4%).
Smaller measurement error does not always lead to improved data worth estimation.
This error does not impact the interpretation or discussion of the data.
The model was verified with wind point data, the relative error did not exceed 6%.
Based on the comparison with other DR methods, the novel robust DR method can effectively improve the reliability of reconciled data even when errors do not follow normal distribution.
Standard diagnostic checks of model fit and residuals were made routinely, and data points with large residuals and/or high influence were investigated for data errors (but we did not exclude any subjects, because all corrected values were considered plausible).
We caution against a conclusion that our data indicate that prediction errors do not play an important role in learning.
The manual error rate was low for several reasons – double entry typically reduces the data error rate significantly, the abstraction was done with case histories and patient information available, and the chart abstractor was familiar with PFT data.
These data confirm the observation that technical errors do not necessarily affect microarray measures.
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