L1 Correlation-Based Penalty in High-Dimensional Quantile Regression


YÜZBAŞI B. , Ahmed S. E. , ASAR Y.

4th International Conference on Big Data and Information Analytics (BigDIA), Texas, United States Of America, 17 - 19 December 2018 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Volume:
  • City: Texas
  • Country: United States Of America

Abstract

In this study, we propose a new method called L1 norm correlation based estimation in quantile regression in high-dimensional sparse models where the number of explanatory variables is large, may be larger than the number of observations, however, only some small subset of the predictive variables are important in explaining the dependent variable. Therefore, the importance of new method is that it addresses both grouping affect and variable selection. Monte Carlo simulations confirm that the new method compares well to the other existing regularization methods.