Robust estimation and classification for functional data via projection-based depth notions

Authors:
Antonio Cuevas;Manuel Febrero;Ricardo Fraiman
Affiliations:
Departamento de Matemáticas, Univ. Autónoma de Madrid, Madrid, Spain;Departamento de Estatística e Inv. Operativa, Univ. de Santiago de Compostela, Santiago de Compostela, Spain;Departamento de Matemática, Univ. de San Andrés, Buenos Aires, Argentina
Venue:
Computational Statistics
Year:
2007

Citing 0
Cited 11

The random Tukey depth

Computational Statistics & Data Analysis
The Tukey and the random Tukey depths characterize discrete distributions

Journal of Multivariate Analysis
Local linear regression for functional predictor and scalar response

Journal of Multivariate Analysis
On depth measures and dual statistics. A methodology for dealing with general data

Journal of Multivariate Analysis
On local times, density estimation and supervised classification from functional data

Journal of Multivariate Analysis
A half-region depth for functional data

Computational Statistics & Data Analysis
Identifying cluster number for subspace projected functional data clustering

Computational Statistics & Data Analysis
Functional density synchronization

Computational Statistics & Data Analysis
Quantiles for finite and infinite dimensional data

Journal of Multivariate Analysis
Interpretable dimension reduction for classifying functional data

Computational Statistics & Data Analysis
Local Mutual Information for Dissimilarity-Based Image Segmentation

Journal of Mathematical Imaging and Vision

Quantified Score

Hi-index	0.00

Visualization

Abstract

Five notions of data depth are considered. They are mostly designed for functional data but they can be also adapted to the standard multivariate case. The performance of these depth notions, when used as auxiliary tools in estimation and classification, is checked through a Monte Carlo study.