Sufficient dimension reduction using Hilbert–Schmidt independence criterion

Yuan Xue, Nan Zhang, Xiangrong Yin and Haitao Zheng

Computational Statistics & Data Analysis, 2017, vol. 115, issue C, 67-78

Abstract: 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.

Keywords: Bootstrap; Central subspace; Hilbert–Schmidt independence criterion; Multiple-index models; Sufficient dimension reduction (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:115:y:2017:i:c:p:67-78

DOI: 10.1016/j.csda.2017.05.002

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