Estimating the geometric median in Hilbert spaces with stochastic gradient algorithms: L p and almost sure rates of convergence

The geometric median, also called $L^1$-median, is often used in robust statistics. Moreover, it is more and more usual to deal with large samples taking values in high dimensional spaces. In this context, a fast recursive estimator has been introduced by Cardot et al. (2013). This work aims at stud...

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Vydáno v:Journal of multivariate analysis Ročník 146; s. 209 - 222
Hlavní autor: Godichon-Baggioni, Antoine
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier 01.04.2016
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ISSN:0047-259X, 1095-7243
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Abstract The geometric median, also called $L^1$-median, is often used in robust statistics. Moreover, it is more and more usual to deal with large samples taking values in high dimensional spaces. In this context, a fast recursive estimator has been introduced by Cardot et al. (2013). This work aims at studying more precisely the asymptotic behavior of the estimators of the geometric median based on such non linear stochastic gradient algorithms. The $L^p$ rates of convergence as well as almost sure rates of convergence of these estimators are derived in general separable Hilbert spaces. Moreover, the optimal rates of convergence in quadratic mean of the averaged algorithm are also given.
AbstractList The geometric median, also called $L^1$-median, is often used in robust statistics. Moreover, it is more and more usual to deal with large samples taking values in high dimensional spaces. In this context, a fast recursive estimator has been introduced by Cardot et al. (2013). This work aims at studying more precisely the asymptotic behavior of the estimators of the geometric median based on such non linear stochastic gradient algorithms. The $L^p$ rates of convergence as well as almost sure rates of convergence of these estimators are derived in general separable Hilbert spaces. Moreover, the optimal rates of convergence in quadratic mean of the averaged algorithm are also given.
Author Godichon-Baggioni, Antoine
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Keywords Robust statistics
Algorithms
Law of large numbers
Spatial median
Martingales in Hilbert space
Stochastic gradient
Functional data analysis
Recursive estimation
Language English
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Title Estimating the geometric median in Hilbert spaces with stochastic gradient algorithms: L p and almost sure rates of convergence
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