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...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
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
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung: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 not be used to model irregular curves such as peak‐like data. Our method is based on a wavelet decomposition of the signal for both fixed and random effects. We propose an efficient dimension reduction step based on wavelet thresholding adapted to multiple curves and using an appropriate structure for the random effect variance, we ensure that both fixed and random effects lie in the same functional space even when dealing with irregular functions that belong to Besov spaces. In the wavelet domain our model resumes to a linear mixed‐effects model that can be used for a model‐based clustering algorithm and for which we develop an EM‐algorithm for maximum likelihood estimation. The properties of the overall procedure are validated by an extensive simulation study. Then, we illustrate our method on mass spectrometry data and we propose an original application of functional data analysis on microarray comparative genomic hybridization (CGH) data. Our procedure is available through the R package curvclust which is the first publicly available package that performs curve clustering with random effects in the high dimensional framework (available on the CRAN).
Bibliographie:http://dx.doi.org/10.1111/j.1541-0420.2012.01828.x
ArticleID:BIOM1828
istex:DA46C681F00076E867D547AA2D796B94FAD29CB6
ark:/67375/WNG-18SRBLZ3-B
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 14
ObjectType-Article-1
ObjectType-Feature-2
content type line 23
ISSN:0006-341X
1541-0420
1541-0420
DOI:10.1111/j.1541-0420.2012.01828.x