Bootstrap estimate of the parameter standard deviation.
s=stdboot(x,T) s=stdboot(x,T,b) [s,y]=stdboot(...)
a matrix of doubles
a function or a list, the function which computes the empirical estimate from x.
a 1-by-1 matrix of doubles, the number of bootstrap resamples (default b=200)
a 1-by-1 matrix of doubles, the estimate of the standard deviation
a 1-by-b matrix of doubles, the values of T of the resamples
Jackknife estimate of the standard deviation of the parameter estimate T(x).
The function T must have the following header:
p=T(x)
x
is the sample or the resample
and p
is a m-by-1 matrix of doubles.
In the case where the parameter estimate has a more general
shape (e.g. 2-by-2), the shape of p
is reshaped
into a column vector with m
components.
See "T and extra arguments" for details on how to pass extra-arguments to T.
The function is equal to
sqrt(diag(covboot(x,T)))
// Estimate the standard deviation of the // empirical mean n = 20; x=distfun_chi2rnd(3,n,1); m=mean(x) // Empirical mean s=stdev(x)/sqrt(n) // Standard error for the mean s=stdboot(x,mean) // Standard error with bootstrap // Get y [s,y]=stdboot(x,mean); size(y) // Set the number of resamples [s,y]=stdboot(x,mean,1000); size(y) // Estimate the standard deviation of the median m=median(x) // Empirical median s=stdboot(x,median) // With extra-arguments for T. x=distfun_chi2rnd(3,20,5); mean(x,"r") s=stdboot(x,list(mean,"r")) | ![]() | ![]() |