Empirically Driven Orthonormal Bases for Functional Data Analysis

DOI10.1007/978-3-030-55874-1_76zbMath1475.62052arXiv2103.07453OpenAlexW3158731438WikidataQ117782825 ScholiaQ117782825MaRDI QIDQ5152875

Hiba Nassar, Krzysztof Podgórski

Publication date: 27 September 2021

Published in: Lecture Notes in Computational Science and Engineering (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2103.07453

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Numerical computation using splines (65D07) Functional data analysis (62R10)

Related Items (2)

Dyadic diagonalization of positive definite band matrices and efficient \(B\)-spline orthogonalization ⋮ Data driven orthogonal basis selection for functional data analysis

Cites Work

• Slepian noise approach for Gaussian and Laplace moving average processes
• Nonparametric functional data analysis. Theory and practice.
• Theoretical Foundations of Functional Data Analysis, with an Introduction to Linear Operators
• A practical guide to splines.
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