Sufficient dimension reduction using Hilbert–Schmidt independence criterion
2017
Xue, Yuan | Zhang, Nan | Yin, Xiangrong | Zheng, Haitao
By using Hilbert–Schmidt Independence Criterion, a sufficient dimension reduction method is proposed to estimate the directions in multiple-index models. A projection pursuit type of sufficient searching algorithm is introduced to reduce the computational complexity, as the original problem involves non-linear optimization over multidimensional Grassmann-manifold. A bootstrap procedure with additional jump point detection algorithm is used for determining the dimensionality. The proposed method demonstrates competitive performance compared with some well-known dimension reduction methods via simulation studies and an application to a real data.
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