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20072024
most citedRobust functional principal components: A projection-pursuit approach

95 citations · 192 across the 22 of their papers we have counts for

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16 papers · 1 filter

stat.ME2024

ROC curve analysis for functional markers

Ana M. Bianco, Graciela Boente, Juan Carlos Pardo-Fernández

Functional markers become a more frequent tool in medical diagnosis. In this paper, we aim to define an index allowing to discriminate between populations when the observations are…

stat.ME2024

Robust Nonparametric Regression for Compositional Data: the Simplicial--Real case

Ana M. Bianco, Graciela Boente, Wenceslao González--Manteiga +2

Statistical analysis on compositional data has gained a lot of attention due to their great potential of applications. A feature of these data is that they are multivariate vectors…

stat.ME2023★ 1 cited

Robust estimation of heteroscedastic regression models: a brief overview and new proposals

Conceição Amado, Ana M. Bianco, Graciela Boente +1

We collect robust proposals given in the field of regression models with heteroscedastic errors. Our motivation stems from the fact that the practitioner frequently faces the confl…

stat.ME2023

Robust estimation for functional logistic regression models

Graciela Boente, Marina Valdora

This paper addresses the problem of providing robust estimators under a functional logistic regression model. Logistic regression is a popular tool in classification problems with…

stat.ME2022

Robust estimation for functional quadratic regression models

Graciela Boente, Daniela Parada

Functional quadratic regression models postulate a polynomial relationship between a scalar response rather than a linear one. As in functional linear regression, vertical and spec…

stat.ME2022

Robust tests for equality of regression curves based on characteristic functions

Graciela Boente, Juan Carlos Pardo-Fernández

This paper focuses on the problem of testing the null hypothesis that the regression functions of several populations are equal under a general nonparametric homoscedastic regressi…

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.ME2021

A robust spline approach in partially linear additive models

Graciela Boente, Alejandra Mercedes Martinez

Partially linear additive models generalize linear ones since they model the relation between a response variable and covariates by assuming that some covariates have a linear rela…

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…

stat.ME2020

A robust approach for ROC curves with covariates

Ana M. Bianco, Graciela Boente, Wenceslao Gonzalez-Manteiga

The Receiver Operating Characteristic (ROC) curve is a useful tool that measures the discriminating power of a continuous variable or the accuracy of a pharmaceutical or medical te…

stat.ME2020★ 25 cited

Robust estimation for semi-functional linear regression models

Graciela Boente, Matias Salibian-Barrera, Pablo Vena

Semi-functional linear regression models postulate a linear relationship between a scalar response and a functional covariate, and also include a non-parametric component involving…

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.ME2019★ 2 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…

stat.ME2017

Robust estimation in single index models when the errors have a unimodal density with unknown nuisance parameter

Claudio Agostinelli, Ana M. Bianco, Graciela Boente

In this paper, we propose a robust profile estimation method for the parametric and nonparametric components of a single index model when the errors have a strongly unimodal densit…

stat.ME2015

Marginal integration estimators for additive models

Graciela Boente, Alejandra Martinez

Additive regression models have a long history in multivariate nonparametric regression. They provide a model in which each regression function depends only on a single explanatory…

stat.ME2007★ 34 cited

Robust estimates in generalized partially linear models

Graciela Boente, Xuming He, Jianhui Zhou

In this paper, we introduce a family of robust estimates for the parametric and nonparametric components under a generalized partially linear model, where the data are modeled by $…