Shrinkage tuning parameter selection with a diverging number of parameters
Hansheng Wang (),
Bo Li () and
Journal of the Royal Statistical Society Series B, 2009, vol. 71, issue 3, 671-683
Summary. Contemporary statistical research frequently deals with problems involving a diverging number of parameters. For those problems, various shrinkage methods (e.g. the lasso and smoothly clipped absolute deviation) are found to be particularly useful for variable selection. Nevertheless, the desirable performances of those shrinkage methods heavily hinge on an appropriate selection of the tuning parameters. With a fixed predictor dimension, Wang and co‐worker have demonstrated that the tuning parameters selected by a Bayesian information criterion type criterion can identify the true model consistently. In this work, similar results are further extended to the situation with a diverging number of parameters for both unpenalized and penalized estimators. Consequently, our theoretical results further enlarge not only the scope of applicabilityation criterion type criteria but also that of those shrinkage estimation methods.
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (72) Track citations by RSS feed
Downloads: (external link)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:bla:jorssb:v:71:y:2009:i:3:p:671-683
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 ().