03610926.2016.1277756.pdf 507 KB
Kudo, Masaki Graduate School of Science and Engineering, Shimane University
Naito, Kanta Graduate School of Science and Engineering, Shimane University
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.
Non parametric regression
Communications in statistics. Theory and methods
Taylor & Francis
This is an Accepted Manuscript of an article published by Taylor & Francis in 'Communications in statistics. Theory and methods' on 2017, available online: http://www.tandfonline.com/doi/full/10.1080/03610926.2016.1277756.
The full-text file will be made open to the public on August 25, 2018 in accordance with publisher's 'Terms and Conditions for Self-Archiving'.
Interdisciplinary Graduate School of Science and Engineering