Abstract
In this paper, the quantile minimum average variance estimator method (QMAVE) and the sparse quantile minimum average variance estimator with lasso penalty (LQMAVE) were proposed. In addition, this paper introduced an inclusive study of QMAVE and LQMAVE. Efficient algorithms proposed to solve QMAVE and LQMAVE minimization problems. The real data analysis and simulations were used to examine the performance of QMAVE and LQMAVE, respectively. From the numerical results, it is clear that the QMAVE and LQMAVE are useful methods in practice.
