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Bandwidth selection for a data sharpening estimator in nonparametric regression

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  • Naito, Kanta
  • Yoshizaki, Masahiro

Abstract

This paper is concerned with data-based selection of the bandwidth for a data sharpening estimator in nonparametric regression. Two kinds of bandwidths are considered: a bandwidth vector which has a different bandwidth for each covariate, and a scalar bandwidth that is common for all covariates. A plug-in method is developed and its theoretical performance is fully investigated. The proposed plug-in method works efficiently in our simulation study.

Suggested Citation

  • Naito, Kanta & Yoshizaki, Masahiro, 2009. "Bandwidth selection for a data sharpening estimator in nonparametric regression," Journal of Multivariate Analysis, Elsevier, vol. 100(7), pages 1465-1486, August.
    • Handle: RePEc:eee:jmvana:v:100:y:2009:i:7:p:1465-1486
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    References listed on IDEAS

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    1. Naito, Kanta, 2001. "On a certain class of nonparametric density estimators with reduced bias," Statistics & Probability Letters, Elsevier, vol. 51(1), pages 71-78, January.
    2. Linton, Oliver & Nielsen, Jens Perch, 1994. "A multiplicative bias reduction method for nonparametric regression," Statistics & Probability Letters, Elsevier, vol. 19(3), pages 181-187, February.
    3. 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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    Cited by:

    1. Wang, Xiaoying & Jiang, Song & Yin, Junping, 2012. "Data sharpening methods in multivariate local quadratic regression," Journal of Multivariate Analysis, Elsevier, vol. 105(1), pages 258-275.

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