Testing Probability Distribution Generators Testing Probability Distribution Generators
In the ‘regression tests’ that are part of any change to the base-R source code, there’s a file called p-r-random-tests.R. People notice it from time... Testing Probability Distribution Generators

In the ‘regression tests’ that are part of any change to the base-R source code, there’s a file called p-r-random-tests.R. People notice it from time to time because the tests sometimes fail. That’s what is supposed to happen.

Testing random number generators is hard, because it’s hard to specify what the results should be: you need statistics. Fortunately, we have statistics, so it’s not impossible. The random tests check that, eg, pnorm() is not ruled out as the cumulative distribution function of numbers from rnorm(). Somewhat unusually, this is a natural use of hypothesis testing with point null and global alternative. The null hypothesis really is that the marginal distribution of numbers from rnorm() is Normal (well, extremely close to Normal), and the alternative hypothesis under a bug in the code is ‘anything else’.

A simple approach would be to use the Kolmogorov-Smirnov test. That’s slightly sub-optimal because the exact sampling distribution is a pain to evaluate for large sample sizes, and even more of a pain for non-continuous distributions. Back in about 1997 I was taking a course on empirical process theory from Jon Wellner, and we learned about Massart’s Inequality, which bounds the error in the empirical cumulative distribution function Fn:

This bound holds for all t, all F, and all n, and the constant 2 in the front is sharp: it’s an optimised version of the Dvoretsky-Kiefer-Wolfowitz inequality, which just says there’s some finite constant that will do. We can generate n random numbers from rwhatever(), compute Fn, and compare it to pwhatever() with the same parameters. If we choose n and t appropriately, we can pick up relatively small errors in either the generator or cdf, with a low false-positive rate.

Here’s the basic code

superror <- function(rfoo,pfoo,sample.size,...) {
    x <- rfoo(sample.size,...)
    tx <- table(signif(x, 12)) # such that xi will be sort(unique(x))
    xi <- as.numeric(names(tx))
    f <- pfoo(xi,...)
    fhat <- cumsum(tx)/sample.size
    max(abs(fhat-f))
}

qdkwbound <- function(n,p) sqrt(log(p/2)/(-2*n))

We run it with n=1000 and p=0.001, so less than one test in 1000 will fail randomly – but that’s still some. Rerunning it (maybe with a larger sample size) is a good response to failure.

So, how different is this from the one-sample Kolmogorov-Smirnov test? Not very. The asymptotic approximaton to the KS is an alternating series with the Massart bound as the first term But it’s somehow elegant to use modern non-asymptotic probability bounds rather than asymptotics plus handwaving in testing basic statistical software functionality.


 

Original Source

Thomas Lumley

Thomas Lumley

Thomas Lumley attended Monash University (B.Sc.(Hons) in Pure Mathematics), the University of Oxford (M.Sc. in Applied Statistics) and the University of Washington, Seattle (PhD in Biostatistics). He spent twelve years on the faculty of the Department of Biostatistics at the University of Washington, and then moved to Auckland in 2010. He is still an Affiliate Professor at the University of Washington.