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Penalizing function based bandwidth choice in nonparametric quantile regression

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  • Abberger, Klaus

Abstract

In nonparametric mean regression various methods for bandwidth choice exist. These methods can roughly be divided into plug-in methods and methods based on penalizing functions. This paper uses the approach based on penalizing functions and adapt it to nonparametric quantile regression estimation, where bandwidth choice is still an unsolved problem. Various criteria for bandwitdth choice are defined and compared in some simulation examples.

Suggested Citation

  • Abberger, Klaus, 2001. "Penalizing function based bandwidth choice in nonparametric quantile regression," CoFE Discussion Papers 01/10, University of Konstanz, Center of Finance and Econometrics (CoFE).
    • Handle: RePEc:zbw:cofedp:0110
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    References listed on IDEAS

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    1. Mammen, Enno, 1990. "A short note on optimal bandwidth selection for kernel estimators," Statistics & Probability Letters, Elsevier, vol. 9(1), pages 23-25, January.
    2. Clifford M. Hurvich & Jeffrey S. Simonoff & Chih‐Ling Tsai, 1998. "Smoothing parameter selection in nonparametric regression using an improved Akaike information criterion," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 60(2), pages 271-293.
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