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On double-index dimension reduction for partially functional data

Author

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  • Guangren Yang
  • Hongmei Lin
  • Heng Lian

Abstract

In this note, we consider the situation where we have a functional predictor as well as some more traditional scalar predictors, which we call the partially functional problem. We propose a semiparametric model based on sufficient dimension reduction, and thus our main interest is in dimension reduction although prediction can be carried out at a second stage. We establish root-n consistency of the linear part of the estimator. Some Monte Carlo studies are carried out as proof of concept.

Suggested Citation

  • Guangren Yang & Hongmei Lin & Heng Lian, 2019. "On double-index dimension reduction for partially functional data," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 31(3), pages 761-768, July.
    • Handle: RePEc:taf:gnstxx:v:31:y:2019:i:3:p:761-768
    DOI: 10.1080/10485252.2019.1632308
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