  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.
