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Selecting the Number of Principal Components in Functional Data

Functional principal component analysis (FPCA) has become the most widely used dimension reduction tool for functional data analysis. We consider functional data measured at random, subject-specific time points, contaminated with measurement error, allowing for both sparse and dense functional data,...

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Bibliografske podrobnosti
Main Authors: Li, Yehua, Wang, Naisyin, Carroll, Raymond J.
Format: Artigo
Jezik:Inglês
Izdano: 2013
Teme:
Online dostop:https://ncbi.nlm.nih.gov/pmc/articles/PMC3872138/
https://ncbi.nlm.nih.gov/pubmed/24376287
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1080/01621459.2013.788980
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