Supplemental Studies for Simultaneous Goodness-of-Fit Testing

TL;DR

This paper introduces a simulation-based p-value adjustment method for goodness-of-fit tests that maintains uniformity under the null hypothesis when multiple tests are performed, improving overall test reliability.

Contribution

It applies a simulation-based p-value adjustment to goodness-of-fit testing, ensuring valid combined inference across multiple tests, a novel approach in this context.

Findings

Adjusted p-values are uniform under the null hypothesis.

The method outperforms individual tests on average across various scenarios.

Simulation studies confirm the effectiveness of the approach.

Abstract

Testing to see whether a given data set comes from some specified distribution is among the oldest types of problems in Statistics. Many such tests have been developed and their performance studied. The general result has been that while a certain test might perform well, aka have good power, in one situation it will fail badly in others. This is not a surprise given the great many ways in which a distribution can differ from the one specified in the null hypothesis. It is therefore very difficult to decide a priori which test to use. The obvious solution is not to rely on any one test but to run several of them. This however leads to the problem of simultaneous inference, that is, if several tests are done even if the null hypothesis were true, one of them is likely to reject it anyway just by random chance. In this paper we present a method that yields a p value that is uniform under the null hypothesis no matter how many tests are run. This is achieved by adjusting the p value via simulation. While this adjustment method is not new, it has not previously been used in the context of goodness-of-fit testing. We present a number of simulation studies that show the uniformity of the p value and others that show that this test is superior to any one test if the power is averaged over a large number of cases.