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.

中文翻译：

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分位数回归的非参数方法。

分位数回归估计条件分位数，在现实世界中具有广泛的应用。估计高条件分位数是一个重要的问题。正则分位数回归（QR）方法通常设计线性或非线性模型，然后估计系数以获得估计的条件分位数。这种方法可能受到线性模型设置的限制。为了克服这个问题，本文提出了一种五步算法的直接非参数分位数回归方法。蒙特卡罗模拟表明，相对于常规 QR 估计器，所提出的直接 QR 估计器具有良好的效率。本文还使用所提出的方法研究了两个现实世界的应用示例。模拟和示例的研究表明，所提出的直接非参数分位数回归模型比常规分位数回归方法更好地拟合数据集。