论文标题
组成协变量的分位数回归
Quantile regression for compositional covariates
论文作者
论文摘要
分位数回归是探索响应变量与其协变量之间关系的非常重要的工具。 lin等人提出的组成协变量的平均回归动机。 （2014年），我们考虑具有无质量和惩罚功能的分数回归。我们根据线性编程开发计算算法。数值研究表明，我们的方法在许多设置下提供了比平均回归更好的选择，尤其是对于误差项的重尾或偏斜分布。最后，我们使用提出的方法研究脂肪数据。
Quantile regression is a very important tool to explore the relationship between the response variable and its covariates. Motivated by mean regression with LASSO for compositional covariates proposed by Lin et al. (2014), we consider quantile regression with no-penalty and penalty function. We develop the computational algorithms based on linear programming. Numerical studies indicate that our methods provides the better alternative than mean regression under many settings, particularly for heavy-tailed or skewed distribution of the error term. Finally, we study the fat data using the proposed method.
