Stable Estimation in Dimension Reduction

作者：Wu Wenbo^*; Yin Xiangrong

来源：Journal of Computational and Graphical Statistics, 2015, 24(1): 104-120.

DOI：10.1080/10618600.2014.896809

• Random weights
• Subsampling
• Grassmann manifold
• Penalized estimator

We introduce stable estimation procedures for several aspects of a sufficient dimension-reduction matrix. We first propose a stable method for estimating structural dimension, which only selects the correct directions in the central subspace with no false positive selection. We then provide a Grassmann manifold sparse estimate for the central subspace. By using subsampling, we develop an ensemble method to obtain a stable nonsparse estimate for the central subspace. This ensemble idea is also used to stabilize the choice of the number of slices in sliced inverse methods. Theoretical results are established, and the efficacy of the proposed stable methods is demonstrated by simulation studies and the analysis of Hitters' salary data. Supplementary materials for this article are available online.

• 出版日期2015-1-2