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bootstrap_estimate

Extract the user specified element from the bootstrapped values.

Available in version 6.4.0 and later.

Prototype

load "$NCARG_ROOT/lib/ncarg/nclscripts/csm/bootstrap.ncl"

	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

See Also

generate_sample_indices, bootstrap_stat, bootstrap_diff, bootstrap_correl, bootstrap_regcoef

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)