Article citationsMore>>

Khan, D. M., Yaqoob, A., Iqbal, N., Wahid, A., Khalil, U., Khan, M. et al. (2019). Variable Selection via SCAD-Penalized Quantile Regression for High-Dimensional Count Data. IEEE Access, 7, 153205-153216.
https://doi.org/10.1109/access.2019.2948278

has been cited by the following article:

• TITLE: A One-Step Variable Selection Procedure for SCAD Penalized Quantile Regression Models

  AUTHORS: Jan G. De Gooijer

  KEYWORDS: High-Dimensional Data, Iterative Least Squares, Quantile Regression, Variable Selection

  JOURNAL NAME: Theoretical Economics Letters, Vol.16 No.1, January 9, 2026

  ABSTRACT: Variable selection using penalized estimation methods in quantile regression models is an important step in screening for relevant covariates. In this paper, we present a one-step estimation procedure for variable selection in sparse, linear additive quantile regression models, using the SCAD penalty. The main idea of the proposed procedure is that the usual L1-norm objective function in quantile regression estimation is replaced by a smooth parametric approximation of this function, via iterative least squares computations. We conduct a simulation study and a real data analysis to check the finite sample performance of the one-step estimator. The results reveal that the one-step quantile SCAD method identifies relevant variables efficiently even when the process under study has heavy tails or if the process is contaminated with outliers.