GANNOUN, Ali; GIRARD, Stéphane; GUINOT, Christiane; SARACCO, Jerôme

doi:10.1016/S0167-9473(03)00141-5

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GANNOUN, Ali
Institut de Mathématiques et de Modélisation de Montpellier [I3M]

GIRARD, Stéphane
Modelling and Inference of Complex and Structured Stochastic Systems [?-2006] [MISTIS [?-2006]]

GUINOT, Christiane
CENTRE DE RECHERCHES ET D'INVESTIGATIONS EPIDERMIQUES ET SENSORIELLES [CERIES]

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Computational Statistics and Data Analysis. 2004, vol. 46, n° 1, p. 103-122

Elsevier

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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.< Réduire

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Sliced Inverse Regression In Reference Curves Estimation

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