Sentence examples for stage threshold from inspiring English sources

Exact(2)

where is the stage threshold, is the weak classifier's weight, and is the total number of weak classifiers (features).

The likelihood ratio test for the first stage threshold estimate is: F 1 = S 0 - S 1 ( γ ̂ 1 ) σ ̂ 2 (6).

Similar(58)

It is important to note that at this stage thresholding is necessary for the opponent because the industrial printers can only print dots, e.g., binary versions of the scanned code.

These optimal stage thresholds suggest that abnormal CSF measures, atrophy rate, cognitive test scores and hippocampal and entorhinal volume provide the best prediction of conversion in ≤2 years, whereas just abnormal CSF, atrophy rate and ADAS-Cog and Rey Auditory Verbal Learning Test scores is the combination that best predicts conversion over a period of 3 to 5 years.

Assuming the first threshold is given, the optimal second-stage threshold estimate is found by minimizing the residual sum of squares for Equation 7. The likelihood ratio test of one versus two thresholds is be based on the statistic: F 2 = S 1 ( γ ̂ 1 ) - S 2 ( γ ̂ 2 ) σ ̂ 2 (8).

The power was calculated based on the probability of passing both the 1st-stage p-value threshold (<5×10−5) and the 2nd-stage threshold (<0.0008 or 0.05/63 by Bonferroni correction) given the effect size.

Likewise, extending the first-stage threshold would have resulted in more false positives being considered in the second stage and higher risks of overparameterization.

With respect to P-value threshold selection at each stage, a lenient first-stage threshold was used to capture most true positive associations at the expense of some false positives.

Our two-stage threshold selection procedure is similar to the procedure that controls both statistical significance and biological significance in [ 86, 88] and we demonstrated that our results were not sensitive to different thresholds of FDR and PER (see Additional file 1, Text S11).

First, it is essential to identify the correct phenotypic staging threshold recognizing that the signature only exists in a subset of tumors that exceed that particular stage.

Figure 1 shows the values of the cumulative hypergeometric probability for the COL11A1 gene using the TCGA ovarian cancer data set and the staging threshold for the definition of the binary phenotype set between IIIb and IIIc.

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