This result was confirmed using a jackknife method, where multiple test datasets were generated by leave-one-out from the Stat3 dataset.
A jackknife method was used to calculate standard errors of parameter estimates.
A jackknife method was used to obtain 95% confidence intervals (CI) on loci, and estimates were calculated assuming random mating and excluded all alleles ≤0.01 [63].
Waples and Do (2008) proposed a jackknife method to empirically estimate the variance of and modify parametric LD confidence intervals accordingly, which should address Problem 1 given an adequate number of loci to compute a jackknife estimate.
The statistical significance of the PDC causality results was accessed through a jackknife method, the trial based leave-one-out method (LOOM) [Schlögl and Supp, 2006].
The C-statistic was calculated to assess the discriminatory capacities of the prediction models for the primary composite outcome, with 95% CI generated by an approximate jackknife method.
Advantages of the "delete-a-group" jackknife method include simplicity of implementation and flexibility to estimate variance for any point estimate of interest.
Further cross-validation was performed by the jackknife method (which does a series of predictions, randomly removing 1 case from the total each time), and by splitting the data set into 2 random halves (one a 'training' set and the other a 'testing' set).
