A roughness model for functional data reveals a phase transition beyond which FPCA loses all information about the underlying variation.
Journal of Multivariate Analysis , volume=
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MATE is a missingness-adaptive thresholding estimator that consistently identifies the number of identifiable factors in high-dimensional incomplete data without imputation.
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Does PCA Work for Rough Functional Data?
A roughness model for functional data reveals a phase transition beyond which FPCA loses all information about the underlying variation.
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Missingness-Adaptive Factor Identification in High-Dimensional Data
MATE is a missingness-adaptive thresholding estimator that consistently identifies the number of identifiable factors in high-dimensional incomplete data without imputation.