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To validate the accuracy of our estimation, we have also estimated confidence intervals using the bootstrap method [16, 17].
As will be discussed later, in nearly all cases our estimations have the expected directional changes, and the estimated confidence intervals provide an additional risk assessment of the likely range of area allocation.
As the losses are estimated, confidence has vanished.
The terms should be ranked according to an estimated confidence of recognition.
The estimated confidence level and estimated power of the D-statistic hypothesis test is studied using Monte Carlo simulation, with different assumptions about the random field, the drift, the scale of variability of the drift and the number of experimental data (i.e. sample size).
For those variants classified as predicted pathogenic, the PSAAP algorithm estimated confidence remained above 90%.
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EL methods for estimating confidence intervals have also been discussed.
It is commonly used to estimate confidence intervals, the bias and variance of the actual dataset.
We apply this observation to estimate confidence intervals for the predictions.
However, the point estimates of the ICERs were negative and negative ratios pose problems when estimating confidence intervals.
The bootstrapping method is advocated as a practical tool for estimating confidence intervals on the provided point estimates of the long return period values.
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