Journal ArticleDOI
The jackknife-a review
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TLDR
In this paper, a review of the literature on the use of the jackknife technique in bias reduction and robust interval estimation is presented, and speculations and suggestions about future research are made.Abstract:
SUMMARY Research on the jackknife technique since its introduction by Quenouille and Tukey is reviewed. Both its role in bias reduction and in robust interval estimation are treated. Some speculations and suggestions about future research are made. The bibliography attempts to include all published work on jackknife methodology.read more
Citations
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Estimating F-statistics for the analysis of population structure.
Bruce S. Weir,C. Clark Cockerham +1 more
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).
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Bootstrap Methods: Another Look at the Jackknife
TL;DR: In this article, the authors discuss the problem of estimating the sampling distribution of a pre-specified random variable R(X, F) on the basis of the observed data x.
Journal ArticleDOI
Bootstrap Methods for Standard Errors, Confidence Intervals, and Other Measures of Statistical Accuracy
Bradley Efron,Robert Tibshirani +1 more
TL;DR: The bootstrap is extended to other measures of statistical accuracy such as bias and prediction error, and to complicated data structures such as time series, censored data, and regression models.
Book
Multilevel Statistical Models
TL;DR: In this article, the authors present a general classification notation for multilevel models and a discussion of the general structure and maximum likelihood estimation for a multi-level model, as well as the adequacy of Ordinary Least Squares estimates.
Journal ArticleDOI
Genome sequence-based species delimitation with confidence intervals and improved distance functions
TL;DR: Despite the high accuracy of GBDP-based DDH prediction, inferences from limited empirical data are always associated with a certain degree of uncertainty, so it is crucial to enrich in-silico DDH replacements with confidence-interval estimation, enabling the user to statistically evaluate the outcomes.
References
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Journal ArticleDOI
Estimation of Error Rates in Discriminant Analysis
TL;DR: In this article, several methods of estimating error rates in discriminant analysis are evaluated by sampling methods, and two methods in most common use are found to be significantly poorer than some new methods that are proposed.