A variant of K nearest neighbor quantile regression

Xuejun Ma, Xiaoqun He and Xiaokang Shi

Journal of Applied Statistics, 2016, vol. 43, issue 3, 526-537

Abstract: Compared with local polynomial quantile regression, K nearest neighbor quantile regression (KNNQR) has many advantages, such as not assuming smoothness of functions. The paper summarizes the research of KNNQR and has carried out further research on the selection of k , algorithm and Monte Carlo simulations. Additionally, simulated functions are Blocks, Bumps, HeaviSine and Doppler, which stand for jumping, volatility, mutagenicity slope and high frequency function. When function to be estimated has some jump points or catastrophe points, KNNQR is superior to local linear quantile regression in the sense of the mean squared error and mean absolute error criteria. To be mentioned, even high frequency, the superiority of KNNQR could be observed. A real data is analyzed as an illustration.

Date: 2016
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DOI: 10.1080/02664763.2015.1070807

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