# Which Bootstrap method is most preferred?

Maybe this question depends on the given data, but is there a "better" bootstrap method than the others? I'm simply using a one variable dataset (that consists of the differences between football scores (2 teams) over the past 15 weeks)..

First note the right skew of this data, I feel like this will take into consideration which bootstrap I would recommend as being "better" or most accurate for the representation of the data.

First here is the standard bootstrap interval

N <- 10^4
n <- length(Differences)
Differences.mean <- numeric(N)
for(i in 1:N)
{
x <- sample(Differences, n, replace = TRUE)
Differences.mean[i]<- mean(x)
}

lower = mean(Differences.mean)-1.96*sd(Differences.mean) #Lower CI
upper = mean(Differences.mean)+1.96*sd(Differences.mean) #Upper CI
= (8.875, 10.916)

mean(Differences.mean)-m  #The bias is fairly small also
= -.0019


Here is a bootstrap percentile interval

 quantile(Differences.mean,c(.025,.975)
= (8.893, 10.938)


Lastly here is the Bootstrap T interval

Tstar = numeric(N)
for(i in 1:N)
{
y =sample(Differences, size = n, replace = TRUE)
Tstar[i] = (mean(y)-m) / (sd(y)/sqrt(n))
}
q1 = quantile(Tstar,.025) #empirical quantiles for bootstrap t (lower)
q2 = quantile(Tstar,.975) #empirical quantiles for bootstrap t (upper)

mean(Differences)-(q2*sd(Differences/sqrt(n)))
mean(Differences)-(q1*sd(Differences/sqrt(n)))
= (8.925, 10.997)


In addition even the t confidence interval seems pretty accurate

 t.test(Differences, conf.level = .95, alternative = "two.sided")
= (8.867, 10.928)


My conclusion would be to choose the bootstrap t interval because it reflects the right skew of the data, it is stretched further to the right than any of the others. My sample size is 224. I think sample size plays a huge role in my conclusion, but my initial question was "is there a better bootstrap method than the others?".. Maybe it really does depend on the data and sample size. Hopefully this isn't too broad.

• Is this a homework problem?
– Jon
Dec 21 '16 at 19:54
– Jon
Dec 21 '16 at 19:55
• It looks like you have some good ideas. Look at Tim Hesterberg's work on bootstrap t. I don't quite understand your graph because it does not show how far on the negative side the curve goes. I am a little surprised at how close the intervals are for all methods. I would have expected the standard bootstrap to do much worse. Dec 21 '16 at 20:25
• Did you consider BC and BCa? Dec 21 '16 at 20:27
• You could look at these books by authors Efron and Tibshirani 1993. Davison and Hinkley 1997 and Chernick 2007. My book was published by Wiley. Efron Chapman and Hall and Davison and Hinkley Cambridge University Press I think. Dec 21 '16 at 20:38