Yuzhu Tian, Qianqian Zhu, Maozai Tian

【Abstract】

By incorporating the Expectation-maximization (EM) algorithm into composite asymmetric Laplace distribution (CALD), an iterative weighted least square estimator for the linear composite quantile regression (CQR) models is derived. Two selection methods for the number of composite quantiles via redefined AIC and BIC are developed. Finally, the proposed procedures are illustrated by some simulations. advantages and drawbacks of the different methods. Simulation studies show that the proposed multiple imputation method performs better.

【Keywords】

CALD, composite quantile regression, EM algorithm, AIC (Akaike’s information criterion), BIC (Bayesian information criterion)