Sufficient dimension reduction using Hilbert–Schmidt independence criterion
Xiangrong Yin and
Computational Statistics & Data Analysis, 2017, vol. 115, issue C, 67-78
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)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:115:y:2017:i:c:p:67-78
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