Penalizing function based bandwidth choice in nonparametric quantile regression
AbstractIn 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.
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Bibliographic InfoPaper provided by Center of Finance and Econometrics, University of Konstanz in its series CoFE Discussion Paper with number 01-10.
Length: 14 pages
Date of creation: Oct 2001
Date of revision:
This paper has been announced in the following NEP Reports:
- NEP-ALL-2006-08-26 (All new papers)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
- Mammen, Enno, 1990. "A short note on optimal bandwidth selection for kernel estimators," Statistics & Probability Letters, Elsevier, vol. 9(1), pages 23-25, January.
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