Bandwidth choice for robust nonparametric scale function estimation
AbstractWe introduce and compare several robust procedures for bandwidth selection when estimating the variance function. These bandwidth selectors are to be used in combination with the robust scale estimates introduced by Boente et al. (2010a). We consider two different robust cross-validation strategies combined with two ways for measuring the cross-validation prediction error. The different proposals are compared with non robust alternatives using Monte Carlo simulation. We also derive some asymptotic results to investigate the large sample performance of the corresponding robust data-driven scale estimators.
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Bibliographic InfoArticle provided by Elsevier in its journal Computational Statistics & Data Analysis.
Volume (Year): 56 (2012)
Issue (Month): 6 ()
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Web page: http://www.elsevier.com/locate/csda
Cross-validation; Data-driven bandwidth; Heteroscedasticity; Local M-estimators; Nonparametric regression; Robust estimation;
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- Boente, Graciela & Ruiz, Marcelo & Zamar, Ruben H., 2010. "On a robust local estimator for the scale function in heteroscedastic nonparametric regression," Statistics & Probability Letters, Elsevier, vol. 80(15-16), pages 1185-1195, August.
- Levine, M., 2006. "Bandwidth selection for a class of difference-based variance estimators in the nonparametric regression: A possible approach," Computational Statistics & Data Analysis, Elsevier, vol. 50(12), pages 3405-3431, August.
- Boente, Graciela & Rodriguez, Daniela, 2008. "Robust bandwidth selection in semiparametric partly linear regression models: Monte Carlo study and influential analysis," Computational Statistics & Data Analysis, Elsevier, vol. 52(5), pages 2808-2828, January.
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