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Data sharpening on unknown manifold

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  • Masaki Kudo
  • Kanta Naito

Abstract

This article is concerned with data sharpening (DS) technique in nonparametric regression under the setting where the multivariate predictor is embedded in an unknown low-dimensional manifold. Theoretical asymptotic bias is derived, which reveals that the proposed DS estimator has a reduced bias compared to the usual local linear estimator. The asymptotic normality of the DS estimator is also developed. It can be confirmed from simulation and applications to real data that the bias reduction for the DS estimator supported on unknown manifold is evident.

Suggested Citation

  • Masaki Kudo & Kanta Naito, 2017. "Data sharpening on unknown manifold," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 46(23), pages 11721-11744, December.
    • Handle: RePEc:taf:lstaxx:v:46:y:2017:i:23:p:11721-11744
    DOI: 10.1080/03610926.2016.1277756
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