Local Polynomial Quantile Regression With Parametric Features
(2009) Journal of the American Statistical Association — Vol. 104, n° 488, p. 1416-1429 (2009)
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- Abstract
- We propose a new approach to conditional quantile function estimation that combines both parametric and nonparametric techniques. At each design point, a global, possibly incorrect, pilot parametric model is locally adjusted through a kernel smoothing fit. The resulting quantile regression estimator behaves like a parametric estimator when the latter is correct and converges to the nonparametric solution as the parametric start deviates from the true underlying model. We give a Bahadur-type representation of the proposed estimator from which consistency and asymptotic normality are derived under an α-mixing assumption. We also propose a practical bandwidth selector based on the plug-in principle and discuss the numerical implementation of the new estimator. Finally, we investigate the performance of the proposed method via simulations and illustrate the methodology with a data example.
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Citations
El Ghouch, A., & Genton, M. G. (2009). Local Polynomial Quantile Regression With Parametric Features. Journal of the American Statistical Association, 104(488), 1416-1429. https://hdl.handle.net/2078.5/194622 (Original work published 2009)