Sensitivity analysis in functional principal component analysis
Copyright policy )Sensitivity analysis in functional principal component analysis
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. Multiple-case diagnostics is also considered. Applications to meteorological data are presented.
- Okayama University Japan
- Kyoto University Japan
Inference from stochastic processes, empirical influence function, Cook's distance, multiple-case diagnostics, penalized functional principal components, Factor analysis and principal components; correspondence analysis
Inference from stochastic processes, empirical influence function, Cook's distance, multiple-case diagnostics, penalized functional principal components, Factor analysis and principal components; correspondence analysis
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