Kernel-based functional principal components (

By Graciela Boente, Ricardo Fraiman. 2000

112 citations recorded by the source corpus.

Subfields: Applied Mathematics, Computer Vision and Pattern Recognition, Mathematical Physics

View Paper: https://www.semanticscholar.org/paper/57d157763f10fdd9bf833a3d95fe9040589b41b5

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Citations in the corpus

Functional Data Analysis for Sparse Longitudinal Data (2005, background): “Various theoretical properties have been studied by Silverman (1996), Boente and Fraiman (2000), and Kneip and Utikal (2001).”
On properties of functional principal components analysis (2006, methodology): “…to techniques for functional PCA (see for example Brumback and Rice (1998), Cardot (2000), Cardot et al. (2000), Girard (2000), James et al. (2000), Boente and Fraiman (2000) and He et al. (2003)), functional PCA in different metrics (e.g. Ocana et al. (1999)) and applications of PCA to fields as…”
Linear functional regression: The case of fixed design and functional response (2002, background): “For additional case-studies or theoretical developments see, e.g., Boente and Fraiman (2000), Bosq (1991), Brumback and Rice (1998), Dauxois, Pousse and Romain (1982), 2 Fan and Lin (1998), Ferraty and Vieu (1999), Hastie and Mallows (1993), Kneip (1994), Kneip and Gasser (1992), Locantore et al.…”
Functional Data Analysis (2016, methodology): “FPCA for fully observed functional data was studied in Dauxois, Pousse & Romain (1982), Besse & Ramsay (1986); Silverman (1996), Bosq (2000); Boente & Fraiman (2000); Hall & Hosseini-Nasab (2006), and it was explored for densely observed functional data in Castro, Lawton & Sylvestre (1986); Rice &…”
Nonparametric Regression Analysis of Longitudinal Data (2005, result): “Similar theoretical results for another functional principal components approach based on kernel smoothers are provided in [2].”
Functional Modelling and Classification of Longitudinal Data * (2005, background): “The importance of smoothing in the estimation of functional principal components has been emphasized in Rice & Silverman (1991), Capra & Müller (1997), Boente & Fraiman (2000) and Cardot (2000).”
Penalized spline models for functional principal component analysis (2006, methodology): “Since then there has emerged a central tool of functional data analysis; for examples, see Rice and Silverman (1991), Jones and Rice (1992), Silverman (1996), Brumback and Rice (1998), Boente and Fraiman (2000) and Fan and Zhang (2000), among others.”
Functional Modelling and Classification of Longitudinal Data. (2005, background): “The importance of smoothing in the estimation of functional principal components has been emphasized in Rice & Silverman (1991), Capra & Muïler (1997), Boente & Fraiman (2000) and Cardot (2000).”
Prediction in functional linear regression (2006, result): “There are a great many more recent contributions, including those of Brumback and Rice [5], Cardot [7], Cardot, Ferraty and Sarda [8–10], Girard [19], James, Hastie and Sugar [23], Boente and Fraiman [3] and He, Müller and Wang [21].”
COVARIATE ADJUSTED FUNCTIONAL PRINCIPAL COMPONENTS ANALYSIS FOR LONGITUDINAL DATA (2010, background): “…a dense grid of time points [see, e.g., Rao (1958), Dauxois, Pousse and Romain (1982), Besse and Ramsay (1986), Castro, Lawton and Sylvestre (1986), Rice and Silverman (1991), Boente and Fraiman (2000), Bosq (2000), Cardot (2000, 2006), Mas and Menneteau (2003) and Hall and Hosseini-Nasab (2006)].”
Asymptotic distributions of nonparametric regression estimators for longitudinal or functional data (2007, background): “A significant intrinsic difference between two settings lies in the perception that functional data are observed in the continuum without noise [2,3], whereas longitudinal data are observed at sparsely distributed time points and are often subject to experimental error [4].”
Outlier detection in functional data by depth measures, with application to identify abnormal NOx levels (2008, background): “For instance, and among others, Silverman (1996), Boente and Fraiman (2000), and Hall and Hosseini-Nasab (2006) studied functional principal component analysis (FPCA).”
Robust functional principal components: A projection-pursuit approach (2011, methodology): “Later on, Croux and Ruiz-Gazen (2005) derived the influence functions of the resulting principal components, while their asymptotic distribution was studied in Cui, He and Ng (2003). A maximization algorithm for obtaining â was proposed in Croux and Ruiz-Gazen (1996) and adapted for highdimensional ”
A Geometric Approach to Maximum Likelihood Estimation of the Functional Principal Components From Sparse Longitudinal Data (2007, methodology): “Another approach to FPCA is through kernel smoothing of the data (Boente and Fraiman 2000).”
Modelling sparse generalized longitudinal observations with latent Gaussian processes (2008, methodology): “Rice and Silverman (1991) and Boente and Fraiman (2000)). Various aspects of the relationship between functional and longitudinal data are discussed in Staniswalis and Lee (1998), Rice (2004) and Zhao et al. (2004); an early study of modelling longitudinal”