I've been using simulation to investigate the behavior of cumulative density function evaluated at extreme values of a sample.
Functionally, my study has $n$ instrumented fish migrating downstream past a tracking station, according to an unknown probability density function with respect to time. With a sample size of $n$ fish, is it possible to calculate the expected value of the total probability density ($x\times100\%$ of the underlying distribution) represented by the min and max migration times in my sample? The ultimate goal is determining a sample size that will identify the time duration of $x\times100\%$ of the total downstream migration.
By means of brute-force simulation with n=10 and n=40 (and trying a few density functions)...
nsim <- 100000
x10 <- x40 <- list(NA, NA, NA, NA)
for(i in 1:nsim) {
# normal, mean=0, sd=1
x10[[1]][i] <- diff(pnorm(range(rnorm(10))))
x40[[1]][i] <- diff(pnorm(range(rnorm(40))))
# lognormal, meanlog=0, sdlog=1
x10[[2]][i] <- diff(plnorm(range(rlnorm(10))))
x40[[2]][i] <- diff(plnorm(range(rlnorm(40))))
# exponential, rate=0
x10[[3]][i] <- diff(pexp(range(rexp(10))))
x40[[3]][i] <- diff(pexp(range(rexp(40))))
# uniform, min=0, max=1
x10[[4]][i] <- diff(punif(range(runif(10))))
x40[[4]][i] <- diff(punif(range(runif(40))))
}
sapply(x10, mean)
## [1] 0.8181371 0.8182755 0.8181157 0.8182054
sapply(x40, mean)
## [1] 0.9512289 0.9512698 0.9511661 0.9512157
What I find truly interesting is that the total theoretical density doesn't seem to depend on the underlying density function! Not only that, but it seems to converge to $1-\frac{2}{n}$ (though not quite... some trial & error makes it look like $1-\frac{2}{n+1}$??)
Am I neglecting to think of something, or are there some interesting asymptotics at work?
Apologies for incorrect notation/terminology on my part - I've been out of grad school too long! Let me know if I can clarify anything.