Abstract : In order to obtain reference curves for data sets when the covariate is multidimensional, we propose in this paper a new procedure based on dimension-reduction and nonparametric estimation of conditional quantiles. This semiparametric approach combines sliced inverse regression (SIR) and a kernel estimation of conditional quantiles. The asymptotic convergence of the derived estimator is shown. By a simulation study, we compare this procedure to the classical kernel nonparametric one for different dimensions of the covariate. The semiparametric estimator shows the best performance. The usefulness of this estimation procedure is illustrated on a real data set collected in order to establish reference curves for biophysical properties of the skin of healthy French women.
Type de document :
Article dans une revue
Computational Statistics and Data Analysis, Elsevier, 2004, 46 (3), pp.103-122
https://hal.inria.fr/hal-00724646
Contributeur : Stephane Girard
<>
Soumis le : mercredi 22 août 2012 - 10:11:27
Dernière modification le : jeudi 11 janvier 2018 - 06:22:11
Document(s) archivé(s) le : vendredi 23 novembre 2012 - 02:22:37
