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A Note on Shrinkage Sliced Inverse Regression

Liqiang Ni, R. Dennis Cook and Chih-Ling Tsai
Biometrika
Vol. 92, No. 1 (Mar., 2005), pp. 242-247
Published by: Oxford University Press on behalf of Biometrika Trust
Stable URL: http://www.jstor.org/stable/20441180
Page Count: 6
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Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.
A Note on Shrinkage Sliced Inverse Regression
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Abstract

We employ Lasso shrinkage within the context of sufficient dimension reduction to obtain a shrinkage sliced inverse regression estimator, which provides easier interpretations and better prediction accuracy without assuming a parametric model. The shrinkage sliced inverse regression approach can be employed for both single-index and multiple-index models. Simulation studies suggest that the new estimator performs well when its tuning parameter is selected by either the Bayesian information criterion or the residual information criterion.

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