A note on shrinkage sliced inverse regression

Liqiang Ni, R. Dennis Cook and Chih-Ling Tsai

Biometrika, 2005, vol. 92, issue 1, 242-247

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. Copyright 2005, Oxford University Press.

Date: 2005
References: Add references at CitEc
Citations: View citations in EconPapers (18)

Downloads: (external link)
http://hdl.handle.net/10.1093/biomet/92.1.242 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:oup:biomet:v:92:y:2005:i:1:p:242-247

Ordering information: This journal article can be ordered from
https://academic.oup.com/journals

Access Statistics for this article

Biometrika is currently edited by Paul Fearnhead

More articles in Biometrika from Biometrika Trust Oxford University Press, Great Clarendon Street, Oxford OX2 6DP, UK.
Bibliographic data for series maintained by Oxford University Press ().