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Integrative Single-Cell Transcriptomics and Network Pharmacology Analysis of Xiao Qing Long Tang in Pediatric Cough-Variant Asthma. [PDF]

Int J Genomics
Cui M, Li Q, Ding X, Zhou M, Lou X, Xia L.   +5 more
europepmc   +1 more source

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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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Supervised functional principal component analysis

Statistics and Computing, 2017
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Yunlong Nie, Liangliang Wang 0002, Baisen Liu, Jiguo Cao   +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
Besse, Philippe, Ramsay, J. O.
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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.
Yoshihiro Yamanishi, Yutaka Tanaka
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Principal component analysis of infinite variance functional data

Journal of Multivariate Analysis, 2023
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Piotr Kokoszka, Rafal Kulik
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A Wavelet Approach to Functional Principal Component Analysis

1998
The aim of this paper is to approximate the estimates in the principal component analysis of a continuous time stochastic process (functional PCA) by using wavelet methods. A short review of estimating in the functional PCA leads to the problem of solving the integral equation with the covariance function as kernel.
Francisco A. Ocaña, Olga Valenzuela, Ana M. Aguilera   +2 more
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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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Adaptive functional principal components analysis

Journal of the Royal Statistical Society Series B: Statistical Methodology
Abstract Functional data analysis almost always involves smoothing discrete observations into curves, because they are never observed in continuous time and rarely without error. Although smoothing parameters affect the subsequent inference, data-driven methods for selecting these parameters are not well-developed, frustrated by the ...
Valentin Patilea
exaly   +4 more sources