The Jackknife

By | August 2, 2012

When it comes down to estimating parameters the standard error is often forgotten.   This is a slightly more complex way to estimate the standard error.  However, this is a good approach when dealing with complex samples.  This is a simple example of how to calculate a jackknife standard error.  This approach can be extended to a cluster samples.

x = rbeta(100,runif(1,0,10),runif(1,0,10));
x = cbind(x);
msum = matrix(NA,nrow=length(x),ncol=1);

for(i in 1:length(x)){
msum[i,1] = (mean(x[-i]) – mean(x))^2;

}

jk.var = (length(msum)-1)/length(msum)*sum(msum[,1]);
jk.se = sqrt(jk.var);
se = sd(x[,1]);

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