Functional principal component analysis via regularized Gaussian basis expansions and its application to unbalanced data

DOI10.1016/j.jspi.2008.11.002zbMath1160.62062OpenAlexW1984827805MaRDI QIDQ1015889

Sadanori Konishi, Mitsunori Kayano

Publication date: 30 April 2009

Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)

Full work available at URL: http://hdl.handle.net/2324/8726

zbMATH Keywords

regularizationmodel selectionsplineradial basis functionsprotein structurefunctional data analysissmoothing parameter

Mathematics Subject Classification ID

Related Items (5)

Consistent variable selection for functional regression models ⋮ The uselessness of the fast Gauss transform for summing Gaussian radial basis function series ⋮ Penalized PCA approaches for B-spline expansions of smooth functional data ⋮ A note on variable selection in functional regression via random subspace method ⋮ Inference under functional proportional and common principal component models

Uses Software

• fda (R)

Cites Work

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