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The jackknife, the bootstrap, and other resampling plans

Bradley Efron
TLDR
The Delta Method and the Influence Function Cross-Validation, Jackknife and Bootstrap Balanced Repeated Replication (half-sampling) Random Subsampling Nonparametric Confidence Intervals as mentioned in this paper.
Abstract
The Jackknife Estimate of Bias The Jackknife Estimate of Variance Bias of the Jackknife Variance Estimate The Bootstrap The Infinitesimal Jackknife The Delta Method and the Influence Function Cross-Validation, Jackknife and Bootstrap Balanced Repeated Replications (Half-Sampling) Random Subsampling Nonparametric Confidence Intervals.

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Confidence limits on phylogenies: an approach using the bootstrap.

TL;DR: The recently‐developed statistical method known as the “bootstrap” can be used to place confidence intervals on phylogenies and shows significant evidence for a group if it is defined by three or more characters.
Journal ArticleDOI

Estimating F-statistics for the analysis of population structure.

TL;DR: The purpose of this discussion is to offer some unity to various estimation formulae and to point out that correlations of genes in structured populations, with which F-statistics are concerned, are expressed very conveniently with a set of parameters treated by Cockerham (1 969, 1973).
Journal ArticleDOI

Arlequin (version 3.0): An integrated software package for population genetics data analysis

TL;DR: Arlequin ver 3.0 as discussed by the authors is a software package integrating several basic and advanced methods for population genetics data analysis, like the computation of standard genetic diversity indices, the estimation of allele and haplotype frequencies, tests of departure from linkage equilibrium, departure from selective neutrality and demographic equilibrium, estimation or parameters from past population expansions, and thorough analyses of population subdivision under the AMOVA framework.
Journal ArticleDOI

MEGA3: Integrated software for Molecular Evolutionary Genetics Analysis and sequence alignment

TL;DR: An overview of the statistical methods, computational tools, and visual exploration modules for data input and the results obtainable in MEGA is provided.
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