176 – Modeling
On Nonparametric Quantile Regression
Jenny Tieu
Brock University
Mei Ling Huang
Brock University
Quantile regression estimates conditional quantiles and has wide applications in the real world. Estimating high conditional quantiles is an important problem. The regular linear quantile regression (QR) method often sets a linear or non-linear model, then estimates the coefficients to obtain the estimated conditional quantile. This approach may be restricted by the model setting. To overcome this problem, this paper proposes a direct nonparametric quantile regression (QN) method. Monte Carlo simulations show good efficiency for the proposed QN estimator relative to the regular QR estimator. The paper also investigates a real-world example using the proposed QN method. Comparisons of the proposed QN method and existing QR methods are given.