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Also, a bootstrap method is proposed to obtain critical values.
In this work, an Improved Bootstrap method is proposed to improve the traditional theoretical approaches.
The bootstrap method is proposed as a tool to estimate the statistical distribution of the data and employ a confidence interval approach of f2 [ 28].
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A dynamic bootstrap grey method is proposed to handle such problems considering that traditional statistical methods cannot.
In particular, a bootstrap approximation method is proposed to determine the critical values for the null distribution of the test statistic containing unknown tail index.
The proposed bootstrap method is conceptually straightforward, but can be computationally expensive at the genome-wide level.
A bootstrap method is also discussed, and its performance is evaluated through simulations.
A bootstrap method is used to evaluate the stability of the solution.
Bootstrap method is a nonparametric method which requires no assumptions about the data distribution.
In this paper, the bootstrap method was used to estimate the frequency of a signal.
A bootstrap method was incorporated to estimate uncertainty and generate more robust predictions of performance.
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