Mixtures of equispaced normal distributions and their use for testing symmetry in univariate data

TL;DR

This paper introduces a novel mixture model-based statistical test for symmetry in univariate data, utilizing normal mixtures with equispaced components and likelihood ratio testing, validated through simulations and real data examples.

Contribution

It proposes a new symmetry test using normal mixture models with constrained weights, offering an alternative to traditional skewness-based methods.

Findings

The mixture-based test performs well in simulations compared to traditional methods.

Different criteria for selecting the number of mixture components affect test performance.

The method is demonstrated on real data examples.

Abstract

Given a random sample of observations, mixtures of normal densities are often used to estimate the unknown continuous distribution from which the data come. Here we propose the use of this semiparametric framework for testing symmetry about an unknown value. More precisely, we show how the null hypothesis of symmetry may be formulated in terms of normal mixture model, with weights about the centre of symmetry constrained to be equal one another. The resulting model is nested in a more general unconstrained one, with same number of mixture components and free weights. Therefore, after having maximised the constrained and unconstrained log-likelihoods by means of a suitable algorithm, such as the Expectation-Maximisation, symmetry is tested against skewness through a likelihood ratio statistic. The performance of the proposed mixture-based test is illustrated through a Monte Carlo simulation study, where we compare two versions of the test, based on different criteria to select the number of mixture components, with the traditional one based on the third standardised moment. An illustrative example is also given that focuses on real data.