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Functional principal components analysis via penalized rank one approximation [PDF]
, 2008 Two existing approaches to functional principal components analysis (FPCA) are due to Rice and Silverman (1991) and Silverman (1996), both based on maximizing variance but introducing penalization in different ways.
Buja, Andreas +2 more
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Multi-band MEG signatures of BOLD connectivity reorganization during visuospatial attention
NeuroImage, 2021 The functional architecture of the resting brain, as measured with the blood oxygenation level-dependent functional connectivity (BOLD-FC), is slightly modified during task performance.
Chiara Favaretto +6 more
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Local Functional Principal Component Analysis [PDF]
Complex Analysis and Operator Theory, 2007 Covariance operators of random functions are crucial tools to study the way random elements concentrate over their support. The principal component analysis of a random function X is well-known from a theoretical viewpoint and extensively used in practical situations. In this work we focus on local covariance operators.
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Functional Linear Mixed Models for Irregularly or Sparsely Sampled Data [PDF]
, 2015 We propose an estimation approach to analyse correlated functional data which are observed on unequal grids or even sparsely. The model we use is a functional linear mixed model, a functional analogue of the linear mixed model.
Cederbaum, Jona +3 more
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Functional Principal Components Analysis of Shanghai Stock Exchange 50 Index
Discrete Dynamics in Nature and Society, 2014 The main purpose of this paper is to explore the principle components of Shanghai stock exchange 50 index by means of functional principal component analysis (FPCA).
Zhiliang Wang, Yalin Sun, Peng Li
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Mathematics, 2023 This work focuses on functional data presenting spatial dependence. The spatial autocorrelation of stock exchange returns for 71 stock exchanges from 69 countries was investigated using the functional Moran’s I statistic, classical principal component ...
Tzung Hsuen Khoo +2 more
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Conditional Functional Principal Components Analysis [PDF]
Scandinavian Journal of Statistics, 2006 Abstract. This work proposes an extension of the functional principal components analysis (FPCA) or Karhunen–Loève expansion, which can take into account non‐parametrically the effects of an additional covariate. Such models can also be interpreted as non‐parametric mixed effect models for functional data.
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Functional principal component analysis of fMRI data [PDF]
Human Brain Mapping, 2004 AbstractWe describe a principal component analysis (PCA) method for functional magnetic resonance imaging (fMRI) data based on functional data analysis, an advanced nonparametric approach. The data delivered by the fMRI scans are viewed as continuous functions of time sampled at the interscan interval and subject to observational noise, and are used ...
Roberto, Viviani +2 more
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On Properties of Functional Principal Components Analysis [PDF]
Journal of the Royal Statistical Society Series B: Statistical Methodology, 2005 SummaryFunctional data analysis is intrinsically infinite dimensional; functional principal component analysis reduces dimension to a finite level, and points to the most significant components of the data. However, although this technique is often discussed, its properties are not as well understood as they might be.
Hall, Peter, Hosseini-Nasab, Seyed
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Functional principal component analysis of spatially correlated data [PDF]
, 2016 This paper focuses on the analysis of spatially correlated functional data. We propose a parametric model for spatial correlation and the between-curve correlation is modeled by correlating functional principal component scores of the functional data ...
Hooker, Giles, Liu, Chong, Ray, Surajit
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