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bootstrap_estimate

Extract the user specified element from the bootstrapped values.

Available in version 6.4.0 and later.

Prototype

```	function bootstrap_estimate (
xBoot   : numeric,
fpc [1] : float or double,
opt [1] : logical
)

return_val [ numeric value or array  ]
```

Arguments

xBoot

An array containing the bootstrapped values in ascending order as returned by the bootstrap_* functions.

fpc

A scalar between 0 and 1 (0.0 < fpc < 1.0) which specifies the desired level.

opt

Currently not used. Set to False.

Return value

All appropriate meta data are returned. Please use printVarSummary(...) to examine the returned variable.

Description

A simple function that calculates the appropriate index value corresponding to the fraction fpc and extracts that value or array from the xBoot array. All appropriate meta data are returned.

References:

```Computer Intensive Methods in Statistics
P. Diaconis and B. Efron
Scientific American (1983), 248:116-130
doi:10.1038/scientificamerican0583-116
http://www.nature.com/scientificamerican/journal/v248/n5/pdf/scientificamerican0583-116.pdf

An Introduction to the Bootstrap
B. Efron and R.J. Tibshirani, Chapman and Hall (1993)

Bootstrap Methods and Permutation Tests: Companion Chapter 18 to the Practice of Business Statistics
Hesterberg, T. et al (2003)
http://statweb.stanford.edu/~tibs/stat315a/Supplements/bootstrap.pdf

Climate Time Series Analysis: Classical Statistical and Bootstrap Methods
M. Mudelsee (2014) Second edition. Springer, Cham Heidelberg New York Dordrecht London
ISBN: 978-3-319-04449-1, e-ISBN: 978-3-319-04450-7
doi: 10.1007/978-3-319-04450-7
xxxii + 454 pp; Atmospheric and Oceanographic Sciences Library, Vol. 51
```

Examples

Please see the Bootstrap and Resampling application page.

Example 1: Let x(N); y(N), N=100:

```   nBoot       = 1000         ; user set
nDim        = 0            ; or (/0,0/); dimension numbers corresponding to 'N'
opt         = False        ; use all default options

BootStrap   = bootstrap_correl(x, y, nBoot, nDim, opt)
rBoot       = BootStrap[0] ; bootstrapped cross-correlations in ascending order
rBootAvg    = BootStrap[1] ; Average of the bootstrapped cross correlations
rBootStd    = BootStrap[2] ; Bootstrapped standard deviations
delete(BootStrap)          ; no longer needed

rBootLow    = bootstrap_estimate(rBoot, 0.025, False)   ;  2.5% lower confidence bound
rBootMed    = bootstrap_estimate(rBoot, 0.500, False)   ; 50.0% median of bootstrapped estimates
rBootHi     = bootstrap_estimate(rBoot, 0.975, False)   ; 97.5% upper confidence bound

printVarSummary(rBoot)    ; information only
printVarSummary(rBootMed)
```