Points of Significance: Sampling distributions and the bootstrap

By Anthony B. Kulesa, Martin Krzywinski, P. Blainey, Naomi Altman. Nature Methods, 2015

140 citations recorded by the source corpus.

Subfields: Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/58effa5d19fcb679aa123625aee93c668dd08bc8

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In Statistics and Probability

1. T. Whitman

Citations in the corpus

• Points of Significance: Statistics versus machine learning (2018, unknown): “For example, the bootstrap [6] can be used for both statistical inference to improve sampling but also acts as the basis for ensemble methods, such as the RF algorithm.”
• Improved detection of changes in species richness in high diversity microbial communities (2016, background): “The main difficulty with extending this type of analysis to evenness indices is that standard errors in the estimates are rarely available, and bootstrap errors understate true sampling variability (Kulesa et al., 2015).”