A nonparametric approach for quantile regression

• Main Authors: Mei Ling Huang, Christine Nguyen
• Format: Article
• Language: English
• Published: SpringerOpen 2018-07-01
• Series: Journal of Statistical Distributions and Applications
• Subjects: Conditional quantile; Goodness-of-fit; Gumbel’s second kind of bivariate exponential distribution; Nonparametric kernel density estimator; Nonparametric regression; Weighted loss function
• Online Access: http://link.springer.com/article/10.1186/s40488-018-0084-9

Abstract Quantile regression estimates conditional quantiles and has wide applications in the real world. Estimating high conditional quantiles is an important problem. The regular quantile regression (QR) method often designs a linear or non-linear model, then estimates the coefficients to obtain the estimated conditional quantiles. This approach may be restricted by the linear model setting. To overcome this problem, this paper proposes a direct nonparametric quantile regression method with five-step algorithm. Monte Carlo simulations show good efficiency for the proposed direct QR estimator relative to the regular QR estimator. The paper also investigates two real-world examples of applications by using the proposed method. Studies of the simulations and the examples illustrate that the proposed direct nonparametric quantile regression model fits the data set better than the regular quantile regression method.