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A Review and Comparison of Bandwidth Selection Methods for Kernel Regression

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Author Info

  • Max Köhler

    (Georg-August-University Göttingen)

  • Anja Schindler

    (Georg-August-University Göttingen)

  • Stefan Sperlich

    (Université de Genéve)

Abstract

Over the last four decades, several methods for selecting the smoothing parameter, generally called the bandwidth, have been introduced in kernel regression. They differ quite a bit, and although there already exist more selection methods than for any other regression smoother we can still see coming up new ones. Given the need of automatic data-driven bandwidth selectors for applied statistics, this review is intended to explain and compare these methods.

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File URL: http://www2.vwl.wiso.uni-goettingen.de/courant-papers/CRC-PEG_DP_95.pdf
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Bibliographic Info

Paper provided by Courant Research Centre PEG in its series Courant Research Centre: Poverty, Equity and Growth - Discussion Papers with number 95.

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Date of creation: 13 Sep 2011
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Handle: RePEc:got:gotcrc:095

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Related research

Keywords: Kernel regression estimation; Bandwidth Selection; Plug-in; Cross Validation;

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  1. Gonzalez Manteiga, W. & Martinez Miranda, M. D. & Perez Gonzalez, A., 2004. "The choice of smoothing parameter in nonparametric regression through Wild Bootstrap," Computational Statistics & Data Analysis, Elsevier, vol. 47(3), pages 487-515, October.
  2. Hall, Peter, 1990. "Using the bootstrap to estimate mean squared error and select smoothing parameter in nonparametric problems," Journal of Multivariate Analysis, Elsevier, vol. 32(2), pages 177-203, February.
  3. Enno Mammen, . "Comparing nonparametric versus parametric regression fits," Statistic und Oekonometrie 9205, Humboldt Universitaet Berlin.
  4. L. Yang & R. Tschernig, 1999. "Multivariate bandwidth selection for local linear regression," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(4), pages 793-815.
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