Semiparametric two-sample mixture components comparison test - Probabilités et statistiques
Pré-Publication, Document De Travail Année : 2020

Semiparametric two-sample mixture components comparison test

Résumé

We consider in this paper two-component mixture distributions having one known component. This is the case when a gold standard reference component is well known, and when a population contains such a component plus another one with different features. When two populations are drawn from such models, we propose a penalized Chi-squared type testing procedure able to compare pairwise the unknown components, i.e. to test the equality of their residual features densities. An intensive numerical study is carried out from a large range of simulation setups to illustrate the asymptotic properties of our test. Moreover the testing procedure is applied on two real cases: i) mortality datasets, where results show that the test remains robust even in challenging situations where the unknown component only represents a small percentage of the global population, ii) galaxy velocities datasets, where stars luminosity mixed with the Milky Way are compared.
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Dates et versions

hal-02491127 , version 1 (25-02-2020)
hal-02491127 , version 2 (09-05-2022)

Identifiants

  • HAL Id : hal-02491127 , version 1

Citer

Xavier Milhaud, Denys Pommeret, Yahia Salhi, Pierre Vandekerkhove. Semiparametric two-sample mixture components comparison test. 2020. ⟨hal-02491127v1⟩

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