Bent line quantile regression via a smoothing technique
Bent line quantile regression via a smoothing technique
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DOI：
10.1002/sam.11453
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发表时间：
2020-03
期刊：
影响因子：
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通讯作者：
Xiaoying Zhou;Feipeng Zhang
中科院分区：
文献类型：
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作者：
Xiaoying Zhou;Feipeng Zhang
弯曲线分位数回归模型可以用两条不同的直线描述响应变量的条件分位数函数，这两条直线在未知的变化点相交。本文提出了一种通过平滑技术同时估计弯曲线分位数回归模型的变点位置和其他回归系数的新方法。在此基础上，给出了该估计量的渐近性质，并给出了变点存在性的形式检验方法。通过仿真验证了该方法的有限样本性能。我们还通过将其应用于人均国内生产总值(GDP)和出生时预期寿命数据来说明所建议的方法。
A bent line quantile regression model can describe the conditional quantile function of the response variable with two different straight lines, which intersect at an unknown change point. This paper proposes a new approach via a smoothing technique to simultaneously estimate the location of the change point and other regression coefficients for the bent line quantile regression model. Furthermore, the asymptotic properties of the proposed estimator are derived, and a formal test procedure for the existence of a change point is also provided. Simulation studies are carried out to demonstrate the finite sample performance of the proposed method. We also illustrate the proposed method by applying it to the gross domestic product (GDP) per capita and the life expectancy at birth data.