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A variant of K nearest neighbor quantile regression

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  • Xuejun Ma
  • Xiaoqun He
  • Xiaokang Shi

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.

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  • Xuejun Ma & Xiaoqun He & Xiaokang Shi, 2016. "A variant of K nearest neighbor quantile regression," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(3), pages 526-537, March.
    • Handle: RePEc:taf:japsta:v:43:y:2016:i:3:p:526-537
    DOI: 10.1080/02664763.2015.1070807
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    Cited by:

    1. González Ordiano, Jorge Ángel & Gröll, Lutz & Mikut, Ralf & Hagenmeyer, Veit, 2020. "Probabilistic energy forecasting using the nearest neighbors quantile filter and quantile regression," International Journal of Forecasting, Elsevier, vol. 36(2), pages 310-323.

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