Sparse Sliced Inverse Quantile Regression
Ali Alkenani and Tahir R. Dikheel
DOI : 10.3844/jmssp.2016.192.200
Journal of Mathematics and Statistics
Volume 12, Issue 3
The current paper proposes the sliced inverse quantile regression method (SIQR). In addition to the latter this study proposes both the sparse sliced inverse quantile regression method with Lasso (LSIQR) and Adaptive Lasso (ALSIQR) penalties. This article introduces a comprehensive study of SIQR and sparse SIQR. The simulation and real data analysis have been employed to check the performance of the SIQR, LSIQR and ALSIQR. According to the results of median of mean squared error and the absolute correlation criteria, we can conclude that the SIQR, LSIQR and ALSIQR are the more advantageous approaches in practice.
© 2016 Ali Alkenani and Tahir R. Dikheel. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.