Sentence examples for optimal significance from inspiring English sources
Including costs for false positive decisions, the optimization can be extended to determine optimal significance levels that maximize expected utility by balancing type I and type II errors leading to a classical Bayesian decision problem.
In their fundamental lemma, Neyman and Pearson proved that the decision has optimal significance and power for, and only for, likelihood-ratio test functions $F$.
In setting the optimal significance threshold for each marker, we consider the rate of return, or power, which depends on the amount invested, or significance threshold.
We adjusted the method in Dobbin and Simon [ 10] for predicting an optimal significance level for gene selection to avoid assuming that the prevalence of the classes is known.
A grid of t values are evaluated for each total sample size n. 2. For each t above, calculate the optimal significance level cutoff α to use for gene selection [ 10]. 3. Using the optimal α levels to select genes from pooled variance t-tests, develop compound covariate predictors (CCP) [ 11] for each training set.
The optimal corrected significance threshold δ wy * which solves the original optimization problem can then be estimated as the α-quantile of the set { p min (j ) } j = 1 J.
This step used a statistical procedure shown to provide optimal network significance (Lage et al. 2010).
The criteria with detected CNV segments were as follows: (1) neighboring regions with significantly different average intensities, and the significant level we chose is p-value less than 0.001, (2) breakpoints (region boundaries) that yielded the optimal statistical significance (smallest p-value), and (3) detected regions with at least 10 probes.
Basically, the genomic segmentation algorithm finds a segment according to three criteria: 1) neighboring regions have statistically significantly different average intensities (P ≥ 0.00001); 2) breakpoints (region boundaries) were chosen to give optimal statistical significance (smallest P-value); and 3) detected regions must contain a minimum of 15 probes.
Patients with Medicaid coverage were still less likely to receive aspirin (0.92, 0.87-0.98) and, with borderline significance, optimal therapy (0.94, 0.88-1.00), compared to patients without Medicaid coverage.
This analysis was used to establish the optimal pathway enrichment significance p-value cutoff of less than 0.00321 (which corresponds to a FDR ≤ 0.32 as computed using the Benjamini-Hochberg method [ 74]).
