Abstract：Aiming at the mixed effects model， based on the existing double Lasso regularized quantile regression (DLQR)， combined with the MCP penalty， a double MCP regularized quantile regression (DMQR) is proposed. Through the improvement of the penalty method， the fitting effect of the model is greatly improved. When solving the parameters， the alternate iterative algorithm is used to solve the quantile regression of a single MCP penalty each time， and combined with the iterative coordinate descent method (QICD) for non-convex penalty， the calculation speed is greatly improved. In the simulation study of the sparse model， it is found that no matter what the error conditions， DMQR eliminates redundant variables very well， and the effect has been greatly improved compared with DLQR. And when the sparseness of the model is different， good simulation results would be obtained.
2021, Vol. 55 
