• 论文 •

高维混合效应模型的双正则化分位回归方法研究

• 出版日期:2017-07-15 发布日期:2017-07-18

The Research of Dual Regularized Quantile Regression for High Dimensional Mixed Effect Models

Luo Youxi et al.

• Online:2017-07-15 Published:2017-07-18

Abstract: The paper proposes a dual regularized quantile regression method for high dimensional mixed effects model, that is, by applying the L1 penalty to the fixed and random effect coefficients, the method can select the important predictive variables in the mixed effect model meanwhile fully taking into account the impact of unknown random effects. The iterative algorithm designed for parameter estimation not only solves the dilemma of selecting two regularization parameters, but also converges quickly. Computer simulation studies show that the new method is not only robust to the random error distribution, but also has good performance even under different sparse models, especially for the high-dimensional case. In this paper, the characteristics of two regularization parameter selection criteria are compared by simulation results, so as to use them in practical problems. Finally, the proposed method is used to analyze an educational data, and the key factors that affect the students' score at different quantiles are given.