A variant of K nearest neighbor quantile regression

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 simul...

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Bibliographic Details
Published in:Journal of applied statistics 2016-02, Vol.43 (3), p.526-537
Main Authors: Ma, Xuejun, He, Xiaoqun, Shi, Xiaokang
Format: Article
Language:English
Subjects:
Algorithms
Computer simulation
cross-validation
Error detection
Functions (mathematics)
High frequencies
K nearest neighbor regression
local polynomial regression
Mathematical analysis
Mathematical functions
mean absolute error
mean squared error
Monte Carlo simulation
Polynomials
quantile regression
Quantiles
Regression
Regression analysis
Studies
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Summary: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.
ISSN:0266-4763
1360-0532
DOI:10.1080/02664763.2015.1070807