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Effects of load carriage methods on fall risk and gait variability during stair ascent: a functional data analysis approach. [PDF]

Front Bioeng Biotechnol
Zhang X, Li Y, Fang Y, Wu H, Kou D, Gao J, Liu J, Sun Y, Sun Y, Gao Y, Duan L, Yu L.   +11 more
europepmc   +1 more source

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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
exaly   +2 more sources

Functional outlier detection with robust functional principal component analysis

Computational Statistics, 2011
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Pallavi Sawant, Nedret Billor
exaly   +3 more sources

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
openaire   +1 more source

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.
openaire   +1 more source

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
openaire   +2 more sources

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
openaire   +1 more source

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
openaire   +1 more source

Multi-way functional principal components analysis

2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2013
Many examples of multi-way or tensor-valued data, such as in climate studies, neuroimaging, chemometrics, and hyperspectral imaging, are structured meaning that variables are associated with locations. Tensor decompositions, or higher-order principal components analysis (HOPCA), are a classical method for dimension reduction and pattern recognition for
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