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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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Robust principal component analysis for functional data

Nicholas Locantore, +17 more
- 01 Jun 1999 - 
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 (

Graciela Boente, +3 more
- 15 Jul 2000 - 
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

Juan Lucas Bali, +3 more
- 09 Mar 2012 - 
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.
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Robust functional principal components: A projection-pursuit approach

Juan Lucas Bali, +3 more
- 01 Dec 2011 - 
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.
Journal ArticleDOI

Robust Nonparametric Regression Estimation for Dependent Observations

Graciela Boente, +1 more
- 01 Sep 1989 - 
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.