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Effective dimension reduction for sparse functional data

, : Effective dimension reduction for sparse functional data. Biometrika 102(2): 421-437

NlmCategory="UNASSIGNED">We propose a method of effective dimension reduction for functional data, emphasizing the sparse design where one observes only a few noisy and irregular measurements for some or all of the subjects. The proposed method borrows strength across the entire sample and provides a way to characterize the effective dimension reduction space, via functional cumulative slicing. Our theoretical study reveals a bias-variance trade-off associated with the regularizing truncation and decaying structures of the predictor process and the effective dimension reduction space. A simulation study and an application illustrate the superior finite-sample performance of the method.


PMID: 26566293

DOI: 10.1093/biomet/asv006

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