Results 261 to 270 of about 1,002,594 (295)

Fast Multilevel Functional Principal Component Analysis

Journal of Computational and Graphical Statistics, 2022
We introduce fast multilevel functional principal component analysis (fast MFPCA), which scales up to high dimensional functional data measured at multiple visits. The new approach is orders of magnitude faster than and achieves comparable estimation accuracy with the original MFPCA (Di et al., 2009).
Erjia Cui, Ruonan Li, Ciprian M. Crainiceanu, Luo Xiao   +3 more
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Principal Components Analysis of Sampled Functions

Psychometrika, 1986
This paper describes a technique for principal components analysis of data consisting of n functions each observed at p argument values. This problem arises particularly in the analysis of longitudinal data in which some behavior of a number of subjects is measured at a number of points in time.
Besse, Philippe, Ramsay, J. O.
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Supervised functional principal component analysis

Statistics and Computing, 2017
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Yunlong Nie, Liangliang Wang, Baisen Liu, Jiguo Cao   +3 more
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Sensitivity analysis in functional principal component analysis

Computational Statistics, 2005
Penalized functional principal components analysis (PCA) is considered. Sensitivity analysis based on the empirical influence functions (EIF) is discussed. EIFs are calculated for a fixed penalty parameter \(\lambda\) and for \(\lambda\) obtained by cross-validation. Cook's distances are proposed for single-case diagnostics.
Yamanishi, Yoshihiro, Tanaka, Yutaka
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Uncertainty in functional principal component analysis

Journal of Applied Statistics, 2016
ABSTRACTPrincipal component analysis (PCA) and functional principal analysis are key tools in multivariate analysis, in particular modelling yield curves, but little attention is given to questions of uncertainty, neither in the components themselves nor in any derived quantities such as scores.
James Sharpe, Nick Fieller
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Robust Functional Principal Component Analysis

2014
When dealing with multivariate data robust principal component analysis (PCA), like classical PCA, searches for directions with maximal dispersion of the data projected on it. Instead of using the variance as a measure of dispersion, a robust scale estimator s n may be used in the maximization problem.
Juan Lucas Bali, Graciela Boente
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Variable-Domain Functional Principal Component Analysis

Journal of Computational and Graphical Statistics, 2019
We introduce a novel method of principal component analysis for data with varying domain lengths for each functional observation.
Jordan T. Johns, Ciprian Crainiceanu, Vadim Zipunnikov, Jonathan Gellar   +3 more
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Multivariate functional principal component analysis: A normalization approach

Statistica Sinica, 2014
Summary: We propose an extended version of the classical Karhunen-Loève expansion of a multivariate random process, termed a normalized multivariate functional principal component (\(m\mathrm{FPC}_n\)) representation. This takes variations between the components of the process into account and takes advantage of component dependencies through the ...
Chiou, Jeng-Min, Chen, Yu-Ting, Yang, Ya-Fang   +2 more
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Principal Component Analysis in Transfer Function

2016
This chapter explores the transfer function in detail, with multiple linear regressions, and principal component analysis (PCA). Furthermore, it contains the slight description of various types of regression and emphasizes on the PCA and the calculations of principal components (PCs) in detail.
T. M. V. Suryanarayana, P. B. Mistry
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Weighted Supervised Functional Principal Components Analysis

Journal of Information and Computing Science
In functional linear regression, a supervised version of functional principal components analysis (FPCA) can automatically estimate the leading functional principal components (FPCs), which not only represent the major source of variation of the functional predictor but also are simultaneously correlated with the response.
Zewen Zhang, Chunzheng Cao, Shuren Cao
openaire   +1 more source