Abstract
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
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