Zeimbekakis, et al. recently published an article in The American Statistician titled On Misuses of the Kolmogorov–Smirnov Test for One-Sample Goodness-of-Fit. One of the misues they discuss is using the KS test with parameters estimated from the sample. For example, let’s sample some data from a normal distribution.

```
x <- rnorm(200, mean = 8, sd = 8)
c(xbar = mean(x), s = sd(x))
```

```
## xbar s
## 8.333385 7.979586
```

If we wanted to assess the goodness-of-fit of this sample to a normal distribution, the following is a bad way to use the KS test:

`ks.test(x, "pnorm", mean(x), sd(x))`

```
##
## Asymptotic one-sample Kolmogorov-Smirnov test
##
## data: x
## D = 0.040561, p-value = 0.8972
## alternative hypothesis: two-sided
```

The appropriate way to use the KS test is to actually supply hypothesized parameters. For example:

`ks.test(x, "pnorm", 8, 8)`

```
##
## Asymptotic one-sample Kolmogorov-Smirnov test
##
## data: x
## D = 0.034639, p-value = 0.9701
## alternative hypothesis: two-sided
```

The results of both tests are the same. We fail to reject the null hypothesis that the sample is from a Normal distribution with the stated mean and standard deviation. However, the former test is very conservative. Zeimbekakis, et al. show this via simulation. I show a simplified version of this simulation. The basic idea is that if the test were valid, the p-values would be uniformly distributed and the points in the uniform distribution QQ-plot would fall along a diagonal line. Clearly that’s not the case.

```
n <- 200
rout <- replicate(n = 1000, expr = {
x <- rnorm(n, 8 , 8)
xbar <- mean(x)
s <- sd(x)
ks.test(x, "pnorm", xbar, s)$p.value
})
hist(rout, main = "Histogram of p-values")
```

```
qqplot(x = ppoints(n), y = rout, main = "Uniform QQ-plot")
qqline(rout, distribution = qunif)
```

Conclusion: using fitted parameters in place of the true parameters in the KS test yields conservative results. The authors state in the abstract that this “has been ‘discovered’ multiple times.”

When done the right way, the KS test yields uniformly distributed p-values.

```
rout2 <- replicate(n = 1000, expr = {
x <- rnorm(n, 8 , 8)
ks.test(x, "pnorm", 8, 8)$p.value
})
hist(rout2)
```

```
qqplot(x = ppoints(n), y = rout2, main = "Uniform QQ-plot")
qqline(rout2, distribution = qunif)
```

Obviously it’s difficult to know which parameters to supply to the KS test. Above we knew to supply 8 as the mean and standard deviation because that’s what we used to generate the data. But what to do in real life? Zeimbekakis, et al. propose a parametric bootstrap to approximate the null distribution of the KS test statistic. The steps to implement the bootstrap are as follows:

- draw a random sample from the fitted distribution
- get estimates of parameters of random sample
- obtain the empirical distribution function
- calculate the bootstrapped KS statistic
- repeat steps 1 – 4 many times

Let’s do it. The following code is a simplified version of what the authors provide with the paper. Notice they use `MASS::fitdistr()`

to obtain MLE parameter estimates. This returns the same mean for the normal distribution but a slightly smaller (i.e. biased) estimated standard deviation.

```
param <- MASS::fitdistr(x, "normal")$estimate
ks <- ks.test(x, function(x)pnorm(x, param[1], param[2]))
stat <- ks$statistic
B <- 1000
stat.b <- double(B)
n <- length(x)
## bootstrapping
for (i in 1:B) {
# (1) draw a random sample from a fitted dist
x.b <- rnorm(n, param[1], param[2])
# (2) get estimates of parameters of random sample
fitted.b <- MASS::fitdistr(x.b, "normal")$estimate
# (3) get empirical distribution function
Fn <- function(x)pnorm(x, fitted.b[1], fitted.b[2])
# (4) calculate bootstrap KS statistic
stat.b[i] <- ks.test(x.b, Fn)$statistic
}
mean(stat.b >= stat)
```

`## [1] 0.61`

The p-value is the proportion of statistics greater than or equal to the observed statistic calculated with estimated parameters.

Let’s turn this into a function and show that it returns uniformly distributed p-values when used with multiple samples. Again this is a simplified version of the R code the authors generously shared with their paper.

```
ks.boot <- function(x, B = 1000){
param <- MASS::fitdistr(x, "normal")$estimate
ks <- ks.test(x, function(k)pnorm(k, param[1], param[2]))
stat <- ks$statistic
stat.b <- double(B)
n <- length(x)
for (i in 1:B) {
x.b <- rnorm(n, param[1], param[2])
fitted.b <- MASS::fitdistr(x.b, "normal")$estimate
Fn <- function(x)pnorm(x, fitted.b[1], fitted.b[2])
stat.b[i] <- ks.test(x.b, Fn)$statistic
}
mean(stat.b >= stat)
}
```

Now replicate the function with many samples. This takes a moment to run. It took my Windows 11 PC with an Intel i7 chip about 100 seconds to run.

```
rout_boot <- replicate(n = 1000, expr = {
x <- rnorm(n, 8 , 8)
ks.boot(x)
})
```

`hist(rout_boot)`

```
qqplot(x = ppoints(n), y = rout_boot, main = "Uniform QQ-plot")
qqline(rout_boot, distribution = qunif)
```