New Tests of Randomness for Circular Data

TL;DR

This paper introduces two novel statistical tests based on random circular arc graphs to assess the assumption of randomness in circular data, ensuring the validity of circular statistical methods.

Contribution

The paper develops and analyzes two new tests, RCAG-EP and RCAG-DD, for verifying randomness in circular data, with proven properties and demonstrated effectiveness through simulations.

Findings

RCAG-EP and RCAG-DD effectively detect non-randomness in circular data.

RCAG-DD generally shows higher power than RCAG-EP in simulations.

The tests are applicable to real-world circular data scenarios.

Abstract

Randomness or mutual independence is an important underlying assumption for most widely used statistical methods for circular data. Verifying this assumption is essential to ensure the validity and reliability of the resulting inferences. In this paper, we introduce two tests for assessing the randomness assumption in circular statistics, based on random circular arc graphs (RCAGs). We define and analyze RCAGs in detail, showing that their key properties depend solely on the i.i.d. nature of the data and are independent of the particular underlying continuous circular distribution. Specifically, we derive the edge probability and vertex degree distribution of RCAGs under the randomness assumption. Using these results, we construct two tests: RCAG-EP, which is based on edge probability, and RCAG-DD, which relies on the vertex degree distribution. Through extensive simulations, we demonstrate that both tests are effective, with RCAG-DD often exhibiting higher power than RCAG-EP. Additionally, we explore several real-world applications where these tests can be useful.