论文标题
带有纵向数据的分位数回归扭结模型的复合估计
Composite Estimation for Quantile Regression Kink Models with Longitudinal Data
论文作者
论文摘要
开发了扭结模型，以分析回归函数为twostage线性的数据，但在未知阈值下相交。在带有纵向数据的分位数回归中，以前的工作假设未知的阈值参数或扭结点在不同的分位数之间是异质的。但是，在不同的分位数中，尤其是在相邻分位水平的区域中，扭结效应的位置往往相同。忽略这种同质性信息可能会导致估计效率损失。鉴于此，我们通过吸收来自多个分位数的信息来提出一个共同纠结点的复合估计器。此外，我们还开发了SUP-Fikelihood-Ratio测试，以在给定的分位数中检查扭结效果。基于分位数等级得分测试，也开发了共同纠结点的测试持续置信区间。仿真研究表明，所提出的复合扭结估计器与最小平方估计器和单个分位数估计器更具竞争力。我们通过分析体重指数和血压数据集来说明这项工作的实际价值。
Kink model is developed to analyze the data where the regression function is twostage linear but intersects at an unknown threshold. In quantile regression with longitudinal data, previous work assumed that the unknown threshold parameters or kink points are heterogeneous across different quantiles. However, the location where kink effect happens tend to be the same across different quantiles, especially in a region of neighboring quantile levels. Ignoring such homogeneity information may lead to efficiency loss for estimation. In view of this, we propose a composite estimator for the common kink point by absorbing information from multiple quantiles. In addition, we also develop a sup-likelihood-ratio test to check the kink effect at a given quantile level. A test-inversion confidence interval for the common kink point is also developed based on the quantile rank score test. The simulation study shows that the proposed composite kink estimator is more competitive with the least square estimator and the single quantile estimator. We illustrate the practical value of this work through the analysis of a body mass index and blood pressure data set.
