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A bootstrap algorithm based on the theoretical framework of Zhang et al. was constructed [21].
Therefore, the robustness of the hierarchical cluster tree was examined with a bootstrap algorithm based on the 10 most variant genes.
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The proposed algorithm, based on CPER bootstrap method, has been experimentally verified with real data obtained from road traffic noise measurement and it showed a very good stability.
Tracking results are compared to an established algorithm based on the bootstrap method in combination with streamline fiber tractography and to another graph based algorithm.
Aiming at this problem, the authors propose a novel Kalman filtering algorithm based on measurement bootstrapping strategy.
Inspired by their work, we develop an adaptive curvature estimation algorithm based on the wild bootstrap method [15, 39].
At its core, it implements a novel algorithm based on Bayesian inference combined with bootstrap-based confidence estimation and kernel regression.
In particular, drawing on a real traffic noise dataset, firstly outliers are eliminated from the actual noise measurements using an outlier detection algorithm based on K-neighbors distance and then uncertainty range is estimated with the bootstrap-t method.
An algorithm based on bivariate kernel smoothers is used to nonparametrically estimate the surface of the object and bootstrap-based procedures are proposed for testing the null hypothesis.
A phylogenetic tree was constructed using maximum-likelihood (ML) algorithms based on the K2+G+I model with 1000 bootstrap replications using the MEGA 5.2.2 package.
Unless otherwise stated, the bootstrap results are based on 1000 bootstrap samples.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com