Sequential sufficient dimension reduction for large p, small n problems
Xiangrong Yin and
Haileab Hilafu
Journal of the Royal Statistical Society Series B, 2015, vol. 77, issue 4, 879-892
Abstract:
type="main" xml:id="rssb12093-abs-0001">
We propose a new and simple framework for dimension reduction in the large p, small n setting. The framework decomposes the data into pieces, thereby enabling existing approaches for n>p to be adapted to n>p problems. Estimating a large covariance matrix, which is a very difficult task, is avoided. We propose two separate paths to implement the framework. Our paths provide sufficient procedures for identifying informative variables via a sequential approach. We illustrate the paths by using sufficient dimension reduction approaches, but the paths are very general. Empirical evidence demonstrates the efficacy of our paths. Additional simulations and applications are given in an on-line supplementary file.
Date: 2015
References: Add references at CitEc
Citations: View citations in EconPapers (15)
Downloads: (external link)
http://hdl.handle.net/10.1111/rssb.2015.77.issue-4 (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:bla:jorssb:v:77:y:2015:i:4:p:879-892
Ordering information: This journal article can be ordered from
http://ordering.onli ... 1111/(ISSN)1467-9868
Access Statistics for this article
Journal of the Royal Statistical Society Series B is currently edited by P. Fryzlewicz and I. Van Keilegom
More articles in Journal of the Royal Statistical Society Series B from Royal Statistical Society Contact information at EDIRC.
Bibliographic data for series maintained by Wiley Content Delivery ().