Circularly Symmetric Tests of Goodness-of-Fit

TL;DR

This paper introduces circularly symmetric goodness-of-fit tests that mitigate location effects and improve detection of distribution deviations, with theoretical and simulation evidence of their superior performance.

Contribution

It proposes novel circularization techniques for Anderson-Darling and Zhang tests, enhancing their robustness and computational efficiency in goodness-of-fit testing.

Findings

Circularized Zhang test outperforms circularized Anderson-Darling.

Circularized tests outperform their original counterparts.

Asymptotic distributions are weighted sums of squared normal variables.

Abstract

It is realized that existing powerful tests of goodness-of-fit are all based on sorted uniforms and, consequently, can suffer from the confounded effect of different locations and various signal frequencies in the deviations of the distributions under the alternative hypothesis from those under the null. This paper proposes circularly symmetric tests that are obtained by circularizing reweighted Anderson-Darling tests, with the focus on the circularized versions of Anderson-Darling and Zhang test statistics. Two specific types of circularization are considered, one is obtained by taking the average of the corresponding so-called scan test statistics and the other by using the maximum. To a certain extent, this circularization technique effectively eliminates the location effect and allows the weights to focus on the various signal frequencies. A limited but arguably convincing simulation study on finite-sample performance demonstrates that the circularized Zhang method outperforms the circularized Anderson-Darling and that the circularized tests outperform their parent methods. Large-sample theoretical results are also obtained for the average type of circularization. The results show that both the circularized Anderson-Darling and circularized Zhang have asymptotic distributions that are a weighted sum of an infinite number of independent squared standard normal random variables. In addition, the kernel matrices and functions are circulant. As a result, asymptotic approximations are computationally efficient via the fast Fourier transform.