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Journal Articles Journal of Statistical Planning and Inference Year : 2022

Semiparametric two-sample admixture components comparison test: The symmetric case

Abstract

In this paper, we consider admixture models which are 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 chi2-type testing procedure allowing a pairwise comparison of the unknown components, i.e. to test the equality of their residual features densities, under a symmetry condition. A 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 a real-world case: galaxy velocities datasets, where stars heliocentric velocities mixed with the Milky Way are compared.
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Dates and versions

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

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Xavier Milhaud, Denys Pommeret, Yahia Salhi, Pierre Vandekerkhove. Semiparametric two-sample admixture components comparison test: The symmetric case. Journal of Statistical Planning and Inference, 2022, 216, pp.135-150. ⟨10.1016/j.jspi.2021.05.010⟩. ⟨hal-02491127v2⟩
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