Wavelet‐Based Clustering for Mixed‐Effects Functional Models in High Dimension

We propose a method for high‐dimensional curve clustering in the presence of interindividual variability. Curve clustering has longly been studied especially using splines to account for functional random effects. However, splines are not appropriate when dealing with high‐dimensional data and can n...

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Veröffentlicht in:	Biometrics Jg. 69; H. 1; S. 31 - 40
Hauptverfasser:	Giacofci, M, Lambert‐Lacroix, S, Marot, G, Picard, F
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Blackwell Publishers 01.03.2013 Blackwell Publishing Ltd Wiley-Blackwell Wiley
Schlagworte:	Algorithms Applications BIOMETRIC METHODOLOGY Biometrics biometry Cluster Analysis Clustering Comparative genomic hybridization Comparative Genomic Hybridization - methods Computer Simulation Data Interpretation, Statistical Datasets Dimensionality reduction Female Functional data Genomics High dimensional spaces Humans Hybridization Likelihood Functions Mass Spectrometry Mass spectroscopy Methodology microarray technology Mixed models Models, Statistical Ovarian Neoplasms - genetics Signals Simulations Statistical variance Statistics Trucks variance wavelet Wavelet transforms Wavelets Wavelets Functional data Clustering Mixed models
ISSN:	0006-341X, 1541-0420, 1541-0420
Online-Zugang:	Volltext
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