statistics-bootstrap

version 5.0.2 (979 KB) by Andrew Penn
Estimate bias, uncertainty (standard errors and confidence intervals) and test hypotheses (p-values) using bootstrap resampling.

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Updated 24 Oct 2022

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This package of functions can be used to estimate bias, uncertainty (standard errors and confidence intervals) and test hypotheses (p-values) using bootstrap resampling. Variations of the bootstrap are included that improve the accuracy of bootstrap statistics for small samples [1-9].
(This file exchange submission has undergone significant development, where it was formally the ibootci function).
Recommended functions from this package include:
  • boot returns resamples data or indices created by balanced bootstrap or bootknife resampling
  • bootknife performs balanced bootknife resampling and calculates bootstrap bias, standard error and confidence intervals. The interval types supported are simple percentile, bias-corrected and accelerated, or calibrated percentile. This function supports iterated and stratified resampling.
  • bootnhst calculates p-values by bootstrap null-hypothesis significance testing (two-tailed). This function can be used to compare 2 or more (independent) samples in designs with a one-way layout. This function resamples under the null hypothesis.
  • bootmode uses bootstrap to evaluate the likely number of real modes in a distribution
  • bootci is a function for calculating bootstrap confidence intervals. This function is a wrapper of the bootknife function but has the same usage as the bootci function from Matlab's Statistics and Machine Learning toolbox.
  • bootstrp is a function for calculating bootstrap statistics. This function is a wrapper of the bootknife function but has the same usage as the bootstrp function from Matlab's Statistics and Machine Learning toolbox.
At the Octave/MATLAB command prompt, type help function-name for more information about the function and it's input and output arguments.
For samples with complex dependence structures, please consider using the legacy iboot package instead at https://github.com/acp29/iboot
bootci and bootstrp are provided in this package because the equivalent functions in MATLAB's Statistics and Machine Learning Toolbox contain a couple of errors, namely in the calculation of the bias for `cper` and `bca` intervals, and in the calculation of `stud` intervals.
Installation
To install (or test) the statistics-bootstrap package at it's existing location in either Octave or Matlab, follow these steps:
  • Download the package. If it is a compressed file, decompress it.
  • Open Octave or Matlab command prompt.
  • cd to the package directory. (The directory contains a file called 'make.m' and 'install.m')
  • Type make to compile the mex files from source (or use the precompiled binaries if available).
  • Type install. The package will load now (and automatically in the future) when you start Octave/Matlab.
To uninstall, cd to the package directory and type uninstall.
Bibliography
[1] Hesterberg T.C. (2004) Unbiasing the Bootstrap: Bootknife Samplingvs. Smoothing; Proceedings of the Section on Statistics & the Environment. Alexandria, VA: American Statistical Association.
[2] Davison et al. (1986) Efficient Bootstrap Simulation. Biometrika, 73: 555-66
[3] Gleason, J.R. (1988) Algorithms for Balanced Bootstrap Simulations. The American Statistician. Vol. 42, No. 4 pp. 263-266
[4] Efron (1987) Better Bootstrap Confidence Intervals. JASA, 82(397): 171-185
[5] Efron, and Tibshirani (1993) An Introduction to the Bootstrap. New York, NY: Chapman & Hall
[6] Hall, Lee and Young (2000) Importance of interpolation when constructing double-bootstrap confidence intervals. Journal of the Royal Statistical Society. Series B. 62(3): 479-491
[7] Ouysee, R. (2011) Computationally efficient approximation for the double bootstrap mean bias correction. Economics Bulletin, AccessEcon, vol. 31(3), pages 2388-2403.
[8] Davison A.C. and Hinkley D.V (1997) Bootstrap Methods And Their Application. Chapter 3, pg. 104
[9] Hesterberg, Tim (2014), What Teachers Should Know about the Bootstrap: Resampling in the Undergraduate Statistics Curriculum, http://arxiv.org/abs/1411.5279

Cite As

Andrew Penn (2022). statistics-bootstrap (https://github.com/gnu-octave/statistics-bootstrap/releases/tag/v5.0.2), GitHub. Retrieved .

MATLAB Release Compatibility
Created with R2007a
Compatible with R2007a and later releases
Platform Compatibility
Windows macOS Linux

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To view or report issues in this GitHub add-on, visit the GitHub Repository.
To view or report issues in this GitHub add-on, visit the GitHub Repository.