Please use this identifier to cite or link to this item: https://doi.org/10.1023/A:1022422002138
Title: Local linear smoothers using asymmetric kernels
Authors: Chen, S.X. 
Keywords: Beta kernels
Gamma kernels
Local linear smoother
Non-parametric regression
Sparse region
Issue Date: 2002
Citation: Chen, S.X. (2002). Local linear smoothers using asymmetric kernels. Annals of the Institute of Statistical Mathematics 54 (2) : 312-323. ScholarBank@NUS Repository. https://doi.org/10.1023/A:1022422002138
Abstract: This paper considers using asymmetric kernels in local linear smoothing to estimate a regression curve with bounded support. The asymmetric kernels are either beta kernels if the curve has a compact support or gamma kernels if the curve is bounded from one end only. While possessing the standard benefits of local linear smoothing, the local linear smoother using the beta or gamma kernels offers some extra advantages in aspects of having finite variance and resistance to sparse design. These are due to their flexible kernel shape and the support of the kernel matching the support of the regression curve.
Source Title: Annals of the Institute of Statistical Mathematics
URI: http://scholarbank.nus.edu.sg/handle/10635/105203
ISSN: 00203157
DOI: 10.1023/A:1022422002138
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