论文标题
通过基因环境相互作用的复合期望回归
Composite Expectile Regression with Gene-environment Interaction
论文作者
论文摘要
如果误差分布具有异方差，则体现了线性回归的假设。预期回归是在此设置中估算响应变量的有条件期望的强大工具。由于对多个级别的预期回归模型进行了充分的研究，因此我们通过结合不同水平的预期回归以提高功效来提出复合期望回归。在本文中，我们研究了高维度的稀疏复合期望回归。通过实现坐标下降算法来实现它。我们还证明了它的选择和估计一致性。进行仿真以证明其性能，与替代方案相当或更好。我们应用了提出的方法来分析肺腺癌（LUAD）真实数据集，以研究G-E相互作用。
If error distribution has heteroscedasticity, it voliates the assumption of linear regression. Expectile regression is a powerful tool for estimating the conditional expectiles of a response variable in this setting. Since multiple levels of expectile regression modelhas been well studied, we propose composite expectile regression by combining different levels of expectile regression to improve the efficacy. In this paper, we study the sparse composite expectile regression under high dimensional setting. It is realized by implementing a coordinate descent algorithm. We also prove its selection and estimation consistency. Simulations are conducted to demonstrate its performance, which is comparable to or better than the alternatives. We apply the proposed method to analyze Lung adenocarcinoma(LUAD) real data set, investigating the G-E interaction.
