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.
Highlights
It is important to study quantile regression to estimate high conditional quantiles in realworld events Koenker (2005)
The traditional quantile regression is concerned with the estimation of the τ th conditional quantile regression (QR) of y for given x which often sets a linear model as
In order to overcome the limitation of the model setting in (2) in this paer we propose a direct nonparametric quantile regression method which uses the ideas of nonparametric kernel density estimation and nonparametric kernel regression
Summary
It is important to study quantile regression to estimate high conditional quantiles in realworld events Koenker (2005). We construct the following a five-step algorithm of a direct nonparametric quantile regression: Step 1: Estimate the conditional density of y for given x =
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