Efficient estimation of conditional covariance matrices for dimension reduction

Abstract : Let X ∈ Rp andY ∈ R. In this paper,we propose an estimator of the conditional covariancematrix, Cov(E[X|Y]), in an inverse regression setting. Based on the estimation of a quadratic functional, this methodology provides an efficient estimator from a semi parametric point of view.We consider a functional Taylor expansion of Cov(E[X|Y]) under some mild conditions and the effect of using an estimate of the unknown joint distribution. The asymptotic properties of this estimator are also provided.
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Communications in Statistics - Theory and Methods, Taylor & Francis, 2017, 46 (9), pp.4403 - 4424. 〈10.1080/03610926.2015.1083109〉
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Soumis le : mercredi 19 juillet 2017 - 12:07:30
Dernière modification le : vendredi 14 septembre 2018 - 09:16:06

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Sébastien Da Veiga, Jean-Michel Loubes, Maikol Solís. Efficient estimation of conditional covariance matrices for dimension reduction. Communications in Statistics - Theory and Methods, Taylor & Francis, 2017, 46 (9), pp.4403 - 4424. 〈10.1080/03610926.2015.1083109〉. 〈hal-01564965〉

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