Variables selection by the LASSO method. Application to malaria data of Tori-Bossito (Benin)

Bienvenue Kouwaye (1, 2) , Noël Fonton (1) , Fabrice Rossi (2) , Gilles Cottrell (3, 4) , Norbert Mahouton Hounkonnou (5)

1 UAC - Université d’Abomey-Calavi = University of Abomey Calavi
2 SAMM - Statistique, Analyse et Modélisation Multidisciplinaire (SAmos-Marin Mersenne)
3 UPD5 Pharmacie - Faculté de Pharmacie de Paris - Université Paris Descartes
4 MERIT - UMR_D 216 - Mère et enfant en milieu tropical : pathogènes, système de santé et transition épidémiologique
5 ICPMA

This work deals with prediction of anopheles number using environmental and climate variables. The variables selection is performed by GLMM (Generalized linear mixed model) combined with the Lasso method and simple cross validation. Selected variables are debiased while the prediction is generated by simple GLMM. Finally, the results reveal to be qualitatively better, at selection, the prediction point of view than those obtained by the reference method.

Mots clés: Lasso; cross validation; variables selection; prediction

Domaines: Machine Learning [stat.ML]; Applications [stat.AP]; Sciences du Vivant [q-bio]

Bienvenue Kouwaye, Noël Fonton, Fabrice Rossi, Gilles Cottrell, Norbert Mahouton Hounkonnou. Variables selection by the LASSO method. Application to malaria data of Tori-Bossito (Benin). 2016. ⟨hal-01352438⟩