Title | Effective dimension reduction for sparse functional data. |
Publication Type | Journal Article |
Year of Publication | 2015 |
Authors | Yao, F, E Lei, and Y Wu |
Journal | Biometrika |
Volume | 102 |
Issue | 2 |
Pagination | 421-437 |
Date Published | 2015 Jun |
ISSN | 0006-3444 |
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. |
DOI | 10.1093/biomet/asv006 |
Alternate Journal | Biometrika |
Original Publication | Effective dimension reduction for sparse functional data. |
PubMed ID | 26566293 |
PubMed Central ID | PMC4640368 |
Grant List | P01 CA142538 / CA / NCI NIH HHS / United States R01 CA149569 / CA / NCI NIH HHS / United States |
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