Dimension reduction based on the Hellinger integral

Wang, Qin; Yin, Xiangrong and Critchley, Frank (2015). Dimension reduction based on the Hellinger integral. Biometrika, 102(1) pp. 95–106.

DOI: https://doi.org/10.1093/biomet/asu062

Abstract

Sufficient dimension reduction is a useful tool for studying the dependence between a response and a multi-dimensional predictor. In this article, a new formulation is proposed that is based on the Hellinger integral of order two, introduced as a natural measure of the regression information contained in the predictor subspace. The response may be either continuous or discrete. We establish links between local and global central subspaces, and propose an efficient local estimation algorithm. Simulations and an application show that our method compares favourably with existing approaches.

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About

• Item ORO ID
• 45153
• Item Type
• Journal Item
• ISSN
• 1464-3510
• Keywords
• central subspace; Hellinger integral; local central subspace; sufficient dimension reduction
• Academic Unit or School
• Faculty of Science, Technology, Engineering and Mathematics (STEM) > Mathematics and Statistics
  Faculty of Science, Technology, Engineering and Mathematics (STEM)
• Copyright Holders
• © 2014 Biometrika Trust
• Depositing User
• Radka Sabolova