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Sample errors account for more than half of reported events.
When conducting inference for nested models, it is important to control for an existing upward shift of the predicted sample errors.
Second, possible sample errors could affect the study result.
The majority of sample errors result from failure to identify the patient correctly.
The obtained threshold cycles (CT) were averaged across replicates and sample errors computed.
Sample errors are given as standard deviation (SD) and standard error of the mean (SEM).
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I'm talking about something statisticians call sample error.
The results have a margin of sample error of plus or minus 3 percentage points.
Further, we note that these are in-sample errors rather than expected prediction errors estimated by out-of-sample forecasting.
Hence, it is not unexpected that our method is susceptible to finite-sample errors in covariance matrix estimation, which leads to an MSE floor in the high SNR region.
Therefore, denote by (widehat{varvec{varepsilon }}_{Y,d} = (widehat{{varepsilon }}_{d,1}, ldots, widehat{{varepsilon }}_{d,24})') vectors of out-of-sample errors (widehat{{varepsilon }}_{d,h} = Y_{d,h} - widehat{Y}_{d,h}) for given out-of-sample day d.
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