Inference under functional proportional and common principal component models

DOI10.1016/j.jmva.2009.09.009zbMath1178.62065OpenAlexW2072000955MaRDI QIDQ1049549

Mariela Sued, Graciela Boente, Daniela Rodriguez

Publication date: 12 January 2010

Published in: Journal of Multivariate Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jmva.2009.09.009

zbMATH Keywords

eigenfunctionskernel methodsHilbert-Schmidt operatorscommon principal componentsfunctional data analysisproportional model

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Factor analysis and principal components; correspondence analysis (62H25) Asymptotic distribution theory in statistics (62E20) Central limit and other weak theorems (60F05) Applications of operator theory in probability theory and statistics (47N30)

Related Items (7)

A survey of functional principal component analysis ⋮ Eigen-Adjusted Functional Principal Component Analysis ⋮ Testing equality between several populations covariance operators ⋮ Robust estimators under a functional common principal components model ⋮ Discussion of ``Analysis of spatio-temporal mobile phone data: a case study in the metropolitan area of Milan ⋮ Two-sample inference for sparse functional data ⋮ Inference under functional proportional and common principal component models

Uses Software

• fda (R)

Cites Work

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• Functional data analysis
• Methods of canonical analysis for functional data.
• Kernel-based functional principal components
• Applied functional data analysis. Methods and case studies
• Asymptotic theory for common principal component analysis
• Smoothed functional principal components analysis by choice of norm
• Nonparametric functional data analysis. Theory and practice.
• Some Statistical Methods for Comparison of Growth Curves
• Conditional Functional Principal Components Analysis

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