Effective dimension reduction for sparse functional data

F. Yao, E. Lei and Y. Wu

Biometrika, 2015, vol. 102, issue 2, 421-437

Abstract: 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.

Date: 2015
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Citations: View citations in EconPapers (14)

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