A robust approach to longitudinal data analysis

The author introduces robust techniques for estimation, inference and variable selection in the analysis of longitudinal data. She first addresses the problem of the robust estimation of the regression and nuisance parameters, for which she derives the asymptotic distribution. She uses weighted estimating equations to build robust quasi-likelihood functions. These functions are then used to construct a class of test statistics for variable selection. She derives the limiting distribution of these tests and shows its robustness properties in terms of stability of the asymptotic level and power under contamination. An application to a real data set allows her to illustrate the benefits of a robust analysis. Une approche robuste pour I'analyse de donnees longitudinales L'auteure presente des techniques robustes pour l'estimation, l'inference et la selection de variables pour l'analyse de donnees longitudinales. Elle s'interesse d'abord au probleme de l'estimation robuste des parametres de regression et de nuisance, dont elle determine la loi asymptotique. Elle se sert d'equations d'estimation ponderees pour construire des fonctions de quasi-vraisemblance robustes. Elle utilise ensuite ces fonctions pour definir une classe de tests statistiques pour la selection de variables. Elle precise la loi limite de ces tests et en demontre la robustesse en terme de stabilite du seuil et de la puissance asymptotique. Une application concrete lui permet d'illustrer les avantages d'une analyse robuste.

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