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# bootstrap_stat

Bootstrap estimates of a user specified statistic derived from a variable. Available in version 6.4.0 and later.

## Prototype

```	function bootstrap_stat (
z         : numeric,
stat   : integer,
nBoot  : integer,
nDim  [*] : integer,
opt    : logical
)

return_val [ variable of type 'list' containing multiple estimates]
```

## Arguments

z

A numeric array of up to four dimensions: z(N), z(N,:), Z(N,:,:), Z(N,:,:,:). 'N' represents the original sample size.

stat

An integer which specifies the statistic to be bootstrapped. Currently:

• =0 is for the 'mean' ('average')
• =1 is for the 'standard deviation'
• =2 is for the 'variance'
• =3 is for the 'median'
• =4 is for the 'min'
• =5 is for the 'max'
• =6 is for the 'sum'

nBoot

An integer specifying the number of bootstrap data samples to be generated.

nDim

The dimension(s) of z on which to calculate the statistic. Must be consecutive and monotonically increasing.

opt

A logical scalar to which optional attributes may be attached. If opt=False, default values are used. If opt=True and no optional attributes are present, default values will be used. If opt=True then:

• opt@sample_size: specifies the size of the resampled array to be used for the bootstrapped statistics.
• opt@sample_size=N is the default.
• opt@sample_size=n where (n.le.N). Most commonly, when this option is used, n=toint(f*N) where 'f' represents (say) 0.10 to 0.20.

• opt@sample_method: specifies the sampling method to use.
• opt@sample_method=1 specifies sampling-with-replacement. This is the default.
• opt@sample_method=0 specifies sampling-without-replacement.

• opt@rseed1=rseed1: allows user to set the first random seed integer value. Default is to use the system initial random seed. (See: random_setallseed)
• opt@rseed2=rseed2: allows user to set the second random seed integer value. Default is to use the system initial random seed. (See: random_setallseed)
• optrseed3="clock": tells NCL to use the 'date' clock to set the two random seeds. (See: random_setallseed)

## Return value

A variable of type 'list'. Members of a list can be accessed directly. However, it is clearer if the members are explicity extracted and given meaningful names.

```                                    ; typeof(Bootstrap) is 'list'
BootStrap  = bootstrap_stat(z, stat, nBoot, 0, opt)
; For clarity; extract variables from 'list'
zBoot      = BootStrap        ; bootstrapped values in ascending order
zAvg       = BootStrap        ; Calculated Mean(s) of the original sample
zStd       = BootStrap        ; Calculated Sample Standard Deviation(s) of the
delete(BootStrap)                ; no longer needed
```

## Description

Bootstrapping is a statistical method that uses data resampling with replacement (see: generate_sample_indices) to estimate the properties of nearly any statistic. It is particularly useful when dealing with small sample sizes. A key feature is that bootstrapping makes no apriori assumption about the distribution of the sample data.

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); N=100

```
nBoot      = 1000          ; user set
stat=      = 0             ; mean
nDim       = 0             ; dimenion identifier (since x is one-dimensional)
opt        = False         ; all defaults are being used

BootStrap  = bootstrap_stat(x, stat, nBoot, nDim, opt)
; for clarity, explicitly extract the variables from the 'lit'
xBoot      = BootStrap  ; bootstrapped values in ascending order
xAvg       = BootStrap  ; Mean of the bootstrapped values
xStd       = BootStrap  ; Sample Standard Deviation of bootstrapped values
delete(BootStrap)          ; no longer needed

xBootLow   = bootstrap_estimate(xBoot, 0.025, False)   ;  2.5% lower confidence bound
xBootMed   = bootstrap_estimate(xBoot, 0.500, False)   ; 50.0% median of bootstrapped estimates
xBootHi    = bootstrap_estimate(xBoot, 0.975, False)   ; 97.5% upper confidence bound

printVarSummary(xBoot)        ; information only
printVarSummary(xBootMed)     ; examine meta data

```