G
Graciela Boente
Researcher at Facultad de Ciencias Exactas y Naturales
Publications - 71
Citations - 1541
Graciela Boente is an academic researcher from Facultad de Ciencias Exactas y Naturales. The author has contributed to research in topics: Estimator & Asymptotic distribution. The author has an hindex of 20, co-authored 63 publications receiving 1370 citations. Previous affiliations of Graciela Boente include National Scientific and Technical Research Council & University of Buenos Aires.
Papers
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Journal ArticleDOI
Robust principal component analysis for functional data
Nicholas Locantore,James Stephen Marron,Douglas G. Simpson,N. Tripoli,Jin-Ting Zhang,K. L. Cohen,Graciela Boente,Ricardo Fraiman,Babette Brumback,Christophe Croux,Jianqing Fan,Alois Kneip,John I. Marden,Daniel Peña,Javier Prieto,James O. Ramsay,Mariano J. Valderrama,Ana M. Aguilera +17 more
TL;DR: A method for exploring the structure of populations of complex objects, such as images, is considered, and endemic outliers motivate the development of a bounded influence approach to PCA.
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Kernel-based functional principal components (
TL;DR: In this article, a kernel-based smooth estimate of the functional principal components of stochastic processes is proposed for continuous trajectories of continuous processes, and strong consistency and the asymptotic distribution are derived under mild conditions.
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Robust functional principal components: A projection-pursuit approach
TL;DR: In this paper, robust estimators for the principal components are considered by adapting the projection pursuit approach to the functional data setting, which combines robust projection-pursuit with different smoothing methods.
Journal ArticleDOI
Robust functional principal components: A projection-pursuit approach
TL;DR: In this article, robust estimators for principal components are considered by adapting the projection pursuit approach to the functional data setting, which combines robust projection-pursuit with different smoothing methods.
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Robust Nonparametric Regression Estimation for Dependent Observations
Graciela Boente,Ricardo Fraiman +1 more
TL;DR: In this paper, two families of robust nonparametric estimators for regression and autoregression are proposed for mixing processes: (i) estimators based on kernel methods and (ii) estimation based on $k$-nearest neighbor kernel methods.