Testing for univariate two-component Gaussian mixture in practice

Abstract : We consider univariate Gaussian mixtures theory and applications, and particularly the problem of testing the null hypothesis of homogeneity (one component) against two components. Several approaches have been proposed in the literature during the last decades. We focus on two different techniques, one based on the Likelihood-Ratio Test (LRT), and another one based on estimation of the parameters of the mixture grounded on some specific adaptation of the well-known EM algorithm often called the EM-test. We propose in particular a novel methodology allowing application of the LRT in actual situations, by plugging-in estimates that are assumed known in asymptotic setup. We aim to provide useful comparisons between different techniques, together with guidelines for practitioners in order to enable them to use theoretical advances for analysing actual data of realistic sample sizes. We finally illustrate these methods in an application to real data corresponding to the number of days between two events concerning ovarian response and lambing for ewes.
Document type :
Preprints, Working Papers, ...
Complete list of metadatas

Cited literature [52 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/hal-01659771
Contributor : Didier Chauveau <>
Submitted on : Wednesday, February 6, 2019 - 12:42:09 PM
Last modification on : Friday, January 10, 2020 - 9:09:02 PM

File

CGM_TestingMixture_v2.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-01659771, version 2

Citation

Didier Chauveau, Bernard Garel, Sabine Mercier. Testing for univariate two-component Gaussian mixture in practice. 2018. ⟨hal-01659771v2⟩

Share

Metrics

Record views

120

Files downloads

781