For this example we create a small data set that has a Poisson distribution

Make/O/N=50 data1=30+poissonNoise(5)

To run bootstrap, in each iteration we draw (with replacement) 100 numbers and compute standard WaveStats on the drawn sample.

StatsResample/N=100 /ws=2/ITER=5 data1

The results for five iterations are shown in the table:

numPoints 100 100 100 100 100
avg 35.09 35.23 34.95 35.28 35.61
sdev 2.19317 2.19667 2.20365 1.85363 2.14568
rms 35.1578 35.2977 35.0187 35.3282 35.6739
adev 1.83 1.8116 1.832 1.5144 1.6914
skew -0.306796 -0.171549 0.107583 -0.276227 -0.483667
kurt -0.637302 -0.595613 -0.468839 -0.173923 -0.162058
sum 3509 3523 3495 3528 3561
meanL1 34.6548 34.7941 34.5127 34.9122 35.1843
meanL2 35.5252 35.6659 35.3873 35.6478 36.0357

Increasing the number of samples does not change the results substantially:

StatsResample/N=10000 /ws=2/ITER=5 data1

The results for five iterations are shown in the table:

numPoints 10000 10000 10000 10000 10000
avg 35.2169 35.2089 35.2312 35.1799 35.2257
sdev 2.04217 2.05121 2.06934 2.05897 2.04572
rms 35.2761 35.2686 35.2919 35.2401 35.285
adev 1.70327 1.71884 1.72827 1.71993 1.71226
skew -0.212116 -0.199756 -0.192836 -0.177744 -0.215313
kurt -0.424137 -0.473013 -0.439945 -0.458874 -0.450679
sum 352169 352089 352312 351799 352257
meanL1 35.1769 35.1687 35.1906 35.1395 35.1856
meanL2 35.2569 35.2491 35.2718 35.2203 35.2658

Jacknife Example

We can use the Jacknife test to analyze the standard deviation of the same data. For this application our user function can be written in the form:

Function jackFunc(inWave)
    Wave inWave
    WaveStats/Q inWave
    return V_sdev
End

To run the Jacknife test execute:

StatsResample/jckn=jackFunc data1

The results are displayed in the table:

N 50
stdEstimate 1.72
JKEstimate 1.71928
JKTEstimate 1.7551
sigmaJKEstimate 0.165026

It is interesting to observe the differences between the standard deviation obtained by the bootstrap approach (above) to the oen obtained here.

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