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Régression multibloc sur classes latentes. Application à l’usage d’antibiotiques en élevages de lapins

Abstract : The statistical processing of analytical epidemiological data aims to determine the risk factors for a disease or veterinary public health problem. To meet this objective, generalized linear models are used. For observations from different sub-populations, these models exist as latent class models, also known as mixture models. However, in veterinary epidemiology in particular, these methods have three main limitations: (i) the number of observations in a sub-population must be greater than the number of variables, (ii) the variables must have a multi-normal distribution, and (iii) the variables must not have strong multi-collinearity, these hypotheses being rarely satisfied in practice. We propose an extension of the mixture models for a large number of variables that do not satisfy distributional hypothesis. These variables may also have the particularity of being organized into thematic blocks. The proposed method is called multiblock regression on latent classes. It combines the simultaneous search for sub-populations within the observations, as well as local (multiblock) regression models associated with each of these sub-populations. This new method is applied, for example, to the search for risk indicators for antibiotic consumption in French rabbit farms.
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Stéphanie Bougeard, Claire Chauvin, Gilbert Saporta, Ndèye Niang. Régression multibloc sur classes latentes. Application à l’usage d’antibiotiques en élevages de lapins. Epidémiologie et Santé Animale, Association pour l'Etude de l'Epidémiologie des Maladies Animales (AEEMA), 2019, pp.43-53. ⟨hal-02919886⟩

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