Balanced bootstrap resampling.
-- Function File: BOOTSTAT = bootstrp (NBOOT, BOOTFUN, D)
-- Function File: BOOTSTAT = bootstrp (NBOOT, BOOTFUN, D1, ..., DN)
-- Function File: BOOTSTAT = bootstrp (..., 'seed', SEED)
-- Function File: BOOTSTAT = bootstrp (..., 'Options', PAROPT)
-- Function File: [BOOTSTAT, BOOTSAM] = bootstrp (...)
BOOTSTAT = bootstrp (NBOOT, BOOTFUN, D) draws NBOOT bootstrap resamples
from the data D and returns the statistic computed by BOOTFUN in BOOTSTAT
[1]. bootstrp resamples from the rows of a data sample D (column vector
or a matrix). BOOTFUN is a function handle (e.g. specified with @), or a
string indicating the function name. The third input argument is data
(column vector or a matrix), that is used to create inputs for BOOTFUN.
The resampling method used throughout is balanced bootstrap resampling
[2-3].
BOOTSTAT = bootstrp (NBOOT, BOOTFUN, D1,...,DN) is as above except that
the third and subsequent numeric input arguments are data vectors that
are used to create inputs for BOOTFUN.
BOOTSTAT = bootstrp (..., 'seed', SEED) initialises the Mersenne Twister
random number generator using an integer SEED value so that bootci results
are reproducible.
BOOTSTAT = bootstrp (..., 'Options', PAROPT) specifies options that govern
if and how to perform bootstrap iterations using multiple processors (if
the Parallel Computing Toolbox or Octave Parallel package is available).
This argument is a structure with the following recognised fields:
o 'UseParallel': If true, use parallel processes to accelerate
bootstrap computations on multicore machines.
Default is false for serial computation. In MATLAB,
the default is true if a parallel pool
has already been started.
o 'nproc': nproc sets the number of parallel processes
[BOOTSTAT, BOOTSAM] = bootstrp (...) also returns BOOTSAM, a matrix of
indices from the bootstrap. Each column in BOOTSAM corresponds to one
bootstrap sample and contains the row indices of the values drawn from
the nonscalar data argument to create that sample.
Bibliography:
[1] Efron, and Tibshirani (1993) An Introduction to the
Bootstrap. New York, NY: Chapman & Hall
[2] Davison et al. (1986) Efficient Bootstrap Simulation.
Biometrika, 73: 555-66
[3] Booth, Hall and Wood (1993) Balanced Importance Resampling
for the Bootstrap. The Annals of Statistics. 21(1):286-298
bootstrp (version 2023.06.20)
Author: Andrew Charles Penn
https://www.researchgate.net/profile/Andrew_Penn/
Copyright 2019 Andrew Charles Penn
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see http://www.gnu.org/licenses/
The following code
% Input univariate dataset
data = [48 36 20 29 42 42 20 42 22 41 45 14 6 ...
0 33 28 34 4 32 24 47 41 24 26 30 41]';
% Compute 50 bootstrap statistics for the mean and calculate the bootstrap
% standard arror
bootstat = bootstrp (50, @mean, data)
std (bootstat)
Produces the following output
bootstat =
30.385
26.577
32.962
26.885
28.962
27.385
29.538
28.615
36.269
29.192
36.192
35.346
30.923
31.423
30.231
25.692
28.577
27.038
26.269
31.423
30.154
29.654
28.577
32.154
28.385
32.615
24.846
32.269
25.577
29.615
26.923
27.038
26.731
33.923
31.962
26.538
26.846
29.769
27
26.731
29.231
30.885
31.077
30
29.115
31.769
32.423
30.692
29.231
31.077
ans = 2.7164
Package: statistics-resampling