Sparse Sliced Inverse Quantile Regression | Science Publications

Journal of Mathematics and Statistics

Tahir R. Dikheel¹  Ali Alkenani¹ 

关键词: Dimension Reduction;    Variable Selection;    Sliced Inverse Quantile Regression;    Lasso;    Adaptive Lasso;   

DOI  :  10.3844/jmssp.2016.192.200

学科分类：社会科学、人文和艺术（综合）

来源: Science Publications

【 摘 要 】

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.

【 授权许可】

Unknown