Data driven orthogonal basis selection for functional data analysis

DOI10.1016/j.jmva.2021.104868OpenAlexW3211353578WikidataQ117783115 ScholiaQ117783115MaRDI QIDQ2078529

Hiba Nassar, Rani Basna, Krzysztof Podgórski

Publication date: 1 March 2022

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

Full work available at URL: https://doi.org/10.1016/j.jmva.2021.104868

zbMATH Keywords

splinesmachine learningfunctional data analysis

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Functional data analysis (62R10) Multivariate analysis (62Hxx)

Related Items (1)

Dyadic diagonalization of positive definite band matrices and efficient \(B\)-spline orthogonalization

Uses Software

• ElemStatLearn

Cites Work

• Unnamed Item
• Bounded optimal knots for regression splines
• Multivariate adaptive regression splines
• A practical guide to splines
• On the distribution of the largest eigenvalue in principal components analysis
• Penalized PCA approaches for B-spline expansions of smooth functional data
• Bayesian curve-fitting with free-knot splines
• Automatic Bayesian Curve Fitting
• Spatially Adaptive Regression Splines and Accurate Knot Selection Schemes
• Empirically Driven Orthonormal Bases for Functional Data Analysis
• Theoretical Foundations of Functional Data Analysis, with an Introduction to Linear Operators
• Functional Data Analysis for Sparse Longitudinal Data

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