A novel method for multivariate data modelling: Piecewise Generalized EMPR

DOI10.1007/s10910-013-0228-6zbMath1310.92004OpenAlexW1990139081MaRDI QIDQ395279

Metin Demiralp, M. Alper Tunga

Publication date: 29 January 2014

Published in: Journal of Mathematical Chemistry (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10910-013-0228-6

zbMATH Keywords

interpolationapproximationhigh dimensional model representationmultidimensional problemsmultivariate data modelling

Mathematics Subject Classification ID

General biostatistics (92B15)

Related Items (4)

Zero interval limit perturbation expansion for the spectral entities of Hilbert-Schmidt operators combined with most dominant spectral component extraction: formulation and certain technicalities ⋮ Zero interval limit perturbation expansion for the spectral entities of Hilbert-Schmidt operators combined with most dominant spectral component extraction: convergence and confirmative implementations ⋮ A hybrid algorithm with cluster analysis in modelling high dimensional data ⋮ Block tridiagonal matrix enhanced multivariance products representation (BTMEMPR)

Uses Software

• MuPAD
• PERL
• GUI-HDMR

Cites Work

• General formulation of HDMR component functions with independent and correlated variables
• Multivariate data modelling through piecewise generalized HDMR method
• The influence of the support functions on the quality of enhanced multivariance product representation
• Constancy maximization based weight optimization in high dimensional model representation for multivariate functions
• Model independent parametric decision making
• A new approach for data partitioning through high dimensional model representation
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