activity
20172022
most citedPrincipal points and elliptical distributions from the multivariate setting to the functional case

31 citations · 33 across the 5 of their papers we have counts for

collaborators

9 papers

stat.ME2022

Estimators for covariate-adjusted ROC curves with missing biomarkers values

Ana M. Bianco, Graciela Boente, Wenceslao González-Manteiga +1

In this paper, we present three estimators of the ROC curve when missing observations arise among the biomarkers. Two of the procedures assume that we have covariates that allow to…

stat.ME2020

Robust functional principal components for sparse longitudinal data

Graciela Boente, Matias Salibian-Barrera

In this paper we review existing methods for robust functional principal component analysis (FPCA) and propose a new method for FPCA that can be applied to longitudinal data where…

math.ST2020

Robust smoothed canonical correlation analysis for functional data

Graciela Boente, Nadia Kudraszow

This paper provides robust estimators for the first canonical correlation and directions of random elements on Hilbert separable spaces by using robust association and scale measur…

math.PR202031 cited

Principal points and elliptical distributions from the multivariate setting to the functional case

Juan Lucas Bali, Graciela Boente

The principal points of a random vector are defined as a set of points which minimize the expected squared distance between and the nearest point in t…

stat.ME2020

Robust location estimators in regression models with covariates and responses missing at random

Ana M. Bianco, Graciela Boente, Wenceslao González-Manteiga +1

This paper deals with robust marginal estimation under a general regression model when missing data occur in the response and also in some of covariates. The target is a marginal l…

stat.ME20192 cited

Penalized robust estimators in logistic regression with applications to sparse models

Ana M. Bianco, Graciela Boente, Gonzalo Chebi

Sparse covariates are frequent in classification and regression problems and in these settings the task of variable selection is usually of interest. As it is well known, sparse st…