Python statsmodels has an implementation of Lilliefors' test for goodness of fit (i.e. if the parameters of the distribution were obtained from fitting the data and not per-determined as in the original Kolmogorov-Smirnow test). The implementation is based on tabulated p-value distributions from 10 mio simulations for different sample sizes and covers normal and exponential distributions. In Wilks' book "Statistical Methods in the Atmospheric Sciences" some tabulated values are provided for gamma distributions with different shape parameters.
I would like to re-create and extend these tables using the
lilliefors_critical_value_simulation.pycode, which is provided in the statsmodels package. In principle, this is rather straightforward, but there is one step in the code, which is unclear to me, and this concerns z-normalisation of the random samples, which are drawn in each experiment. For the normal and exponential distributions, the code reads:
if sim_type == 'normal': std = sample.std(0, ddof=1) z = (sample - mu) / std cdf_fn = stats.norm.cdf elif sim_type == 'exponential': z = sample / mu cdf_fn = stats.expon.cdf
Indeed, scaling is pretty straightforward. However, for gamma distributions, a z-scale is not well defined (I also came across the term probability integral transform, but also this was limited to normal distributions). Upon research I found that people recommend to use quantiles instead of the standard deviation. I am now wondering, how critical the z-scaling is for the derivation of the p-value statistics, and if a pseudo z-scale
(x - mu)/(75%-ile - 25%-ile) can be safely used in the context of simulating the p-value distribution.
Further information about the implementation details:
The original source code of lilliefors_critical_value_simulation.py can be found at https://github.com/statsmodels/statsmodels/blob/master/statsmodels/stats/tests/results/lilliefors_critical_value_simulation.py. The relevant part begins in line 57, where the CDF of the z-normalized sample is calculated. Then,
minus are constructed as [1/N, ..., N/N] and [0, (N-1)/N], respectively and the differences
cdf-minus are evaluated. The result for this specific sample is then calculated as maximum absolute difference (concatenating
d_minus via the somewhat obstruse
_csyntax; see https://numpy.org/doc/stable/reference/generated/numpy.c_.html). These results are collected for all sample sizes to be tabulated. Lateron (line 83 ff) the critical values are calculated by evaluating the precentiles of the results.
(Or has someone computed extensive tables for gamma distributions already and made them available somewhere?)