Sentence examples for methods the jackknife from inspiring English sources

However, as elucidated by [39] and demonstrated by Eq.50 in [41], among the three cross-validation methods, the jackknife test is deemed the most objective that can always yield a unique result for a given benchmark dataset, and hence has been increasingly used and widely recognized by investigators to examine the accuracy of various predictors (see, e.g. [42] [44]).

However, as elucidated in [18] and demonstrated by Eq.50 of [12], among the three cross-validation methods, the jackknife test is deemed the most objective that can always yield a unique result for a given benchmark dataset and hence has been increasingly and widely adopted to examine the power of various predictors (see, e.g., [42], [46], [51], [53], [55], [65], [66], [67], [68], [69]).

However, among the three test methods, the jackknife test is deemed the least arbitrary that can always yield a unique result for a given benchmark dataset [ 18].

However, as elucidated by a comprehensive review [ 24], among the three cross-validation methods, the jackknife test was deemed the least arbitrary and most objective because it could always yield a unique result for a given benchmark dataset and hence has been increasingly used and widely recognized by investigators to examine the accuracy of various predictors (see, e.g., [ 60, 61, 90, 99– 101]).

Among the three methods, the jackknife test is deemed the least arbitrary and most objective as elucidated in [ 44] and hence has been widely recognized and increasingly adopted by investigators to examine the quality of various predictors [ 31, 34, 45– 50].

Among these three methods, the jackknife test is deemed the most objective and rigorous one that can exclude the memory effects during the entire testing process and can always yield a unique result for a given benchmark dataset, as elucidated in [ 59] and demonstrated by Equation  50 of Chou and Shen [ 60].

To evaluate our method, the jackknife cross-validating test is employed on two widely-used datasets, 25PDB and 1189 datasets with sequence similarity lower than 40%and25%5%, respectively.

In the first method, the jackknife test is a CV technique in which one dataset sequence is used as a testing data (for validation of the generated model) while the remaining dataset sequences are used as the training data to develop a model.

The method uses the Jackknife method in which each entry is taken from the list and identified against each group.

According to the calculation process, we list the performance results of our method using the jackknife test when k = 29 in Tables 4 and 5 (i.e., to say N1 + N2 + N3 + N4 = 29).

Since a large number of source-detector pairs was involved in this work a quantitative automated method, somewhat akin to the jackknife method [ 39], was developed to identify and remove, or 'prune' the noisy detector channels, as described in more detail here below.