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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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| Main Authors: | , , |
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| Format: | Artigo |
| Language: | Inglês |
| Published: |
2013
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| Subjects: | |
| Online Access: | 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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