Nonlinear sufficient dimension reduction for functional data

Bing Li, Jun Song

Research output: Contribution to journalArticlepeer-review

19 Scopus citations

Abstract

We propose a general theory and the estimation procedures for nonlinear sufficient dimension reduction where both the predictor and the response may be random functions. The relation between the response and predictor can be arbitrary and the sets of observed time points can vary from subject to subject. The functional and nonlinear nature of the problem leads to construction of two functional spaces: the first representing the functional data, assumed to be a Hilbert space, and the second characterizing nonlinearity, assumed to be a reproducing kernel Hilbert space. A particularly attractive feature of our construction is that the two spaces are nested, in the sense that the kernel for the second space is determined by the inner product of the first. We propose two estimators for this general dimension reduction problem, and establish the consistency and convergence rate for one of them. These asymptotic results are flexible enough to accommodate both fully and partially observed functional data.We investigate the performances of our estimators by simulations, and applied them to data sets about speech recognition and handwritten symbols.

Original languageEnglish (US)
Pages (from-to)1059-1095
Number of pages37
JournalAnnals of Statistics
Volume45
Issue number3
DOIs
StatePublished - Jun 2017

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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