Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/61788
Authors: 
Chen, Song Xi
Year of Publication: 
1999
Series/Report no.: 
Discussion Papers, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes 1999,100
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 kernel 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.
Subjects: 
nonparametric regression
Beta kernels
Gamma kernels
local linear smoother
Sparse region
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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