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ISSN: 1549-3644 (Print)
ISSN: 1558-6359 (Online)
Research Article Open Access

Sparse Sliced Inverse Quantile Regression

Ali Alkenani1 and Tahir R. Dikheel1
  • 1 University of Al-Qadisiyah, Iraq

Abstract

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.

Journal of Mathematics and Statistics
Volume 12 No. 3, 2016, 192-200

DOI: https://doi.org/10.3844/jmssp.2016.192.200

Submitted On: 5 February 2016 Published On: 10 September 2016

How to Cite: Alkenani, A. & Dikheel, T. R. (2016). Sparse Sliced Inverse Quantile Regression. Journal of Mathematics and Statistics, 12(3), 192-200. https://doi.org/10.3844/jmssp.2016.192.200

Open AccessCopyright: © 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.

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Keywords

  • Dimension Reduction
  • Variable Selection
  • Sliced Inverse Quantile Regression
  • Lasso
  • Adaptive Lasso