Uniform consistency in number of neighbors of the kNN estimator of the conditional quantile model

Ali Laksaci (), Elias Ould Saïd () and Mustapha Rachdi ()
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Ali Laksaci: King Khalid University
Elias Ould Saïd: I.U.T. de Calais
Mustapha Rachdi: Université Grenoble Alpes (France)

Metrika: International Journal for Theoretical and Applied Statistics, 2021, vol. 84, issue 6, No 5, 895-911

Abstract: Abstract We are interested in the efficiency of the nonparametric estimation of the conditional quantile when the response variable is a scalar given a functional covariate. To do this, we adopt a technique which is based on the use of the k-Nearest Neighbors procedure to build a kernel estimator of this model. Then, we establish the uniform convergence in number of neighbors of the constructed estimator. Moreover, we discuss the optimal choices of different parameters that are involved in the model as well as the impacts of the obtained results. Finally, we show the applicability and efficiency of our methodology to investigate the fuel quality by using a Near-infrared spectroscopy dataset.

Keywords: Almost complete convergence rate; Functional data analysis; Functional nonparametric statistics; kNN method; Quantile regression; UNN consistency; Fuel quality; Near-infrared spectroscopy; 62G05; 62G08; 62G10; 62G35; 62G07 (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s00184-021-00806-5

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