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Local Linear Smoothers Using Asymmetric Kernels

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  • Song Chen

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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. --

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File URL: http://hdl.handle.net/10.1023/A:1022422002138
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Bibliographic Info

Article provided by Springer in its journal Annals of the Institute of Statistical Mathematics.

Volume (Year): 54 (2002)
Issue (Month): 2 (June)
Pages: 312-323

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Handle: RePEc:spr:aistmt:v:54:y:2002:i:2:p:312-323

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Related research

Keywords: Beta kernels; gamma kernels; local linear smoother; nonparametric regression; sparse region;

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Cited by:
  1. Matthias Hagmann & Olivier Scaillet, 2004. "Local Multiplicative Bias Correction For Asymmetric Kernel Density Estimators," Royal Economic Society Annual Conference 2004 25, Royal Economic Society.
  2. Nikolay Gospodinov & Masayuki Hirukawa, 2008. "Time Series Nonparametric Regression Using Asymmetric Kernels with an Application to Estimation of Scalar Diffusion Processes," CIRJE F-Series CIRJE-F-573, CIRJE, Faculty of Economics, University of Tokyo.
  3. Fe, Eduardo, 2012. "Efficient estimation in regression discontinuity designs via asymmetric kernels," MPRA Paper 38164, University Library of Munich, Germany.
  4. Taoufik Bouezmarni & Jeroen V.K. Rombouts, 2006. "Nonparametric Density Estimation for Positive Time Series," Cahiers de recherche 06-09, HEC Montréal, Institut d'économie appliquée.

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