A plug-in bandwidth selector for nonparametric quantile regression
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Mercedes Conde-Amboage [1] ; César Sánchez-Sellero [1]
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[1] Universidade de Santiago de Compostela
Universidade de Santiago de Compostela
Santiago de Compostela, España
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- Localización: Test: An Official Journal of the Spanish Society of Statistics and Operations Research, ISSN-e 1863-8260, ISSN 1133-0686, Vol. 28, Nº. 2, 2019, págs. 423-450
- Idioma: inglés
- DOI: 10.1007/s11749-018-0582-6
- Texto completo no disponible (Saber más ...)
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Resumen
In the framework of quantile regression, local linear smoothing techniques have been studied by several authors, particularly by Yu and Jones (J Am Stat Assoc 93:228–237, 1998). The problem of bandwidth selection was addressed in the literature by the usual approaches, such as cross-validation or plug-in methods. Most of the plug-in methods rely on restrictive assumptions on the quantile regression model in relation to the mean regression, or on parametric assumptions. Here we present a plug-in bandwidth selector for nonparametric quantile regression that is defined from a completely nonparametric approach. To this end, the curvature of the quantile regression function and the integrated squared sparsity (inverse of the conditional density) are both nonparametrically estimated. The new bandwidth selector is shown to work well in different simulated scenarios, particularly when the conditions commonly assumed in the literature are not satisfied. A real data application is also given.
