Sufficient dimension reduction (SDR) turns out to be a useful dimension reduction tool in high-dimensional regression analysis. Weisberg (2002) developed the dr-package to implement the four most popular SDR methods. However, the package does not provide any clear guidelines as to which method should be used given a data. Since the four methods may provide dramatically different dimension reduction results, the selection in the dr-package is problematic for statistical practitioners. In this paper, a basis-adaptive selection algorithm is developed in order to relieve this issue. The basic idea is to select an SDR method that provides the highest correlation between the basis estimates obtained by the four classical SDR methods. A real data example and numerical studies confirm the practical usefulness of the developed algorithm.
Supplementary materials are available in addition to this article. It can be downloaded at RJ-2018-045.zip
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For attribution, please cite this work as
Yoo, "Basis-Adaptive Selection Algorithm in dr-package", The R Journal, 2018
BibTeX citation
@article{RJ-2018-045,
author = {Yoo, Jae Keun},
title = {Basis-Adaptive Selection Algorithm in dr-package},
journal = {The R Journal},
year = {2018},
note = {https://doi.org/10.32614/RJ-2018-045},
doi = {10.32614/RJ-2018-045},
volume = {10},
issue = {2},
issn = {2073-4859},
pages = {124-132}
}