Factor-based comparison of groups of curves

Authors:
Frédéric Ferraty;Philippe Vieu;Sylvie Viguier-Pla
Affiliations:
Laboratoire de Statistique et Probabilités, Université Paul Sabatier, UMR C5583-118 route de Narbonne, 31062 Toulouse Cedex 04, France;Laboratoire de Statistique et Probabilités, Université Paul Sabatier, UMR C5583-118 route de Narbonne, 31062 Toulouse Cedex 04, France;Laboratoire de Statistique et Probabilités, Université Paul Sabatier, UMR C5583-118 route de Narbonne, 31062 Toulouse Cedex 04, France
Venue:
Computational Statistics & Data Analysis
Year:
2007

Citing 3
Cited 5

Common principal components & related multivariate models

Common principal components & related multivariate models
Nonparametric Functional Data Analysis: Theory and Practice (Springer Series in Statistics)

Nonparametric Functional Data Analysis: Theory and Practice (Springer Series in Statistics)
Impartial trimmed k-means for functional data

Computational Statistics & Data Analysis

Editorial: Statistics for Functional Data

Computational Statistics & Data Analysis
Bootstrapping sums of independent but not identically distributed continuous processes with applications to functional data

Journal of Multivariate Analysis
Structural components in functional data

Computational Statistics & Data Analysis
Interpretable dimension reduction for classifying functional data

Computational Statistics & Data Analysis
No effect tests in regression on functional variable and some applications to spectrometric studies

Computational Statistics

Quantified Score

Hi-index	0.03

Visualization

Abstract

A method able to detect a possible common structure between two (or more than two) groups of curves is considered. This technique is part of the recent developments within the field of Functional Data Analysis. It is discussed how the standard approach based on factorial analysis for comparing groups of multivariate data can be used in this infinite-dimensional framework. The potentiality of such a functional method in terms of application is illustrated through a spectrometric functional data set coming from food industry quality control.