Non-asymptotic detection of two-component mixtures with unknown means

TL;DR

This paper introduces a non-asymptotic, adaptive testing procedure for detecting two-component mixtures with unknown means, demonstrating optimal power and strong performance in simulations.

Contribution

It proposes a novel multiple testing method based on order statistic spacings that adapts to mixture proportions and mean differences, with proven optimality.

Findings

The test maintains level-$\\alpha$ for all sample sizes.

It adapts automatically to mixture parameters.

Simulation results show superior performance over classical methods.

Abstract

This work is concerned with the detection of a mixture distribution from a $\mathbb{R}$-valued sample. Given a sample $X_1,\dots,X_n$ and an even density $\phi$, our aim is to detect whether the sample distribution is $\phi(\cdot-\mu)$ for some unknown mean $\mu$, or is defined as a two-component mixture based on translations of $\phi$. We propose a procedure which is based on several spacings of the order statistics, which provides a level-$\alpha$ test for all $n$. Our test is therefore a multiple testing procedure and we prove from a theoretical and practical point of view that it automatically adapts to the proportion of the mixture and to the difference of the means of the two components of the mixture under the alternative. From a theoretical point of view, we prove the optimality of the power of our procedure in various situations. A simulation study shows the good performances of our test compared with several classical procedures.