Sparse Sliced Inverse Regression via Lasso

For multiple index models, it has recently been shown that the sliced inverse regression (SIR) is consistent for estimating the sufficient dimension reduction (SDR) space if and only if , where p is the dimension and n is the sample size. Thus, when p is of the same or a higher order of n, additiona...

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Bibliographic Details
Published in:	Journal of the American Statistical Association Vol. 114; no. 528; pp. 1726 - 1739
Main Authors:	Lin, Qian, Zhao, Zhigen, Liu, Jun S.
Format:	Journal Article
Language:	English
Published:	United States Taylor & Francis 02.10.2019 Taylor & Francis Group, LLC Taylor & Francis Ltd
Subjects:	Algorithms Analysis of covariance Convergence Covariance matrix Dimension reduction Eigenvectors High dimensional statistics Minimax Noise Regression Regression analysis sample size Signal to noise ratio Sparsity Statistical methods Statistics Theory and Methods Theory of large deviation variance covariance matrix
ISSN:	0162-1459, 1537-274X, 1537-274X
Online Access:	Get full text
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