Accurate and Efficient Approximation of the Null Distribution of Rao's Spacing Test

TL;DR

This paper introduces a recursive moment-based approximation method for Rao's spacing test null distribution, enabling accurate p-value computation for any sample size without relying on pre-tabulated critical values.

Contribution

We develop a novel recursive moment calculation combined with Gram-Charlier expansion to accurately approximate Rao's spacing test distribution for arbitrary sample sizes.

Findings

Method achieves high accuracy across various sample sizes.

Eliminates dependence on limited critical value tables.

Effective for large and small sample sizes.

Abstract

Rao's spacing test is a widely used nonparametric method for assessing uniformity on the circle. However, its broader applicability in practical settings has been limited because the null distribution is not easily calculated. As a result, practitioners have traditionally depended on pre-tabulated critical values computed for a limited set of sample sizes, which restricts the flexibility and generality of the method. In this paper, we address this limitation by recursively computing higher-order moments of the Rao's spacing test statistic and employing the Gram-Charlier expansion to derive an accurate approximation to its null distribution. This approach allows for the efficient and direct computation of p-values for arbitrary sample sizes, thereby eliminating the dependency on existing critical value tables. Moreover, we confirm that our method remains accurate and effective even for large sample sizes that are not represented in current tables, thus overcoming a significant practical limitation. Comparative evaluations with published critical values and saddlepoint approximations demonstrate that our method achieves a high degree of accuracy across a wide range of sample sizes. These findings greatly improve the practicality and usability of Rao's spacing test in both theoretical investigations and applied statistical analyses.