Title
A Note On Shrinkage Sliced Inverse Regression
Keywords
Garotte; Lasso; Shrinkage estimator; Sliced inverse regression; Sufficient dimension reduction
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. © 2005 Biometrika Trust.
Publication Date
3-1-2005
Publication Title
Biometrika
Volume
92
Issue
1
Number of Pages
242-247
Document Type
Article
Personal Identifier
scopus
DOI Link
https://doi.org/10.1093/biomet/92.1.242
Copyright Status
Unknown
Socpus ID
15844375310 (Scopus)
Source API URL
https://api.elsevier.com/content/abstract/scopus_id/15844375310
STARS Citation
Ni, Liqiang; Cook, R. Dennis; and Tsai, Chih Ling, "A Note On Shrinkage Sliced Inverse Regression" (2005). Scopus Export 2000s. 4080.
https://stars.library.ucf.edu/scopus2000/4080