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Functional principal component analysis

Functional principal component analysis (FPCA) is a statistical method for investigating the dominant modes of variation of functional data. Using… 
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Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
2017
2017
This article is motivated by some longitudinal clinical data of kidney transplant recipients, where kidney function progression… 
2016
2016
Many of the existing data-driven human motion synthesis methods rely on statistical modeling of motion capture data. Motion… 
2016
2016
This paper decomposes the risk premia of individual stocks into contributions from systematic and idiosyncratic risks. I… 
2014
2014
This paper focuses on the analysis of spatially correlated functional data. The between-curve correlation is modeled by… 
2014
2014
This paper reconsiders the challenge of analysing coordination in human movement with particular emphasis on the application of… 
2013
2013
In this paper a new construction of functional principal components (FPCA) is proposed, based on principal components for vector… 
2005
2005
SummaryIn the present paper empirical influence functions (EIFs) are derived for eigenvalues and eigenfunctions in functional… 
1999
1999
The objective of this paper is to apply functional principal component analysis to model and forecast financial prices of the… 
Review
1998
Review
1998
The aim of this paper is to approximate the estimates in the principal component analysis of a continuous time stochastic process…