- Publisher: Al-Qadisiyah Journal for Administrative and Economic Sciences
- Available in: PDF
- DOI: 10.33961/21.2.2019/1-11
- Published: May 18, 2021
Sparse dimension reduction via penalized quantile MAVE
Revised : 14\4\2019 Accepted :30\5\2019
Doaa Tahir Malik1 and Ali Alkenani2
Department of Statistics,
College of Administration and Economics, University of Al-Qadisiyah, Iraq.
|Corresponding Author: Doaa Tahir Malik1 and Ali Alkenani2|
|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.
Keywords:Dimension regression, Quantile regression, MAVE, Quantile MAVE, Lasso.