# File "Rboot4".
B = 1000
n = 100
# Generate some data.
x = runif(n)
thetahatlist = NULL
# Run the bootstrap, with B resamplings of n resamples each.
for (b in 1:B) {
# Do n bootstrap resamples.
resampled = rep(0,n)
for (i in 1:n) {
j = floor( runif(1,1,n+1) ) # uniform on {1,2,...,n}
resampled[i] = x[j]
}
# Compute thetahat for this resample.
thetahat = max(resampled)
thetahatlist = c(thetahatlist, thetahat)
}
# Compute the bootstrap estimate of mean, thetastar.
thetastar = sum(thetahatlist) / B
# Compute the bootstrap estimate of variance.
sqsum = 0
for (i in 1:B) {
sqsum = sqsum + (thetahatlist[i] - thetastar)^2
}
varest = sqsum / (B-1)
print(varest)