Please use this identifier to cite or link to this item: https://doi.org/10.1111/j.1467-9868.2008.00693.x
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dc.titleShrinkage tuning parameter selection with a diverging number of parameters
dc.contributor.authorWang, H.
dc.contributor.authorLi, B.
dc.contributor.authorLeng, C.
dc.date.accessioned2014-10-28T05:15:11Z
dc.date.available2014-10-28T05:15:11Z
dc.date.issued2009-06
dc.identifier.citationWang, H., Li, B., Leng, C. (2009-06). Shrinkage tuning parameter selection with a diverging number of parameters. Journal of the Royal Statistical Society. Series B: Statistical Methodology 71 (3) : 671-683. ScholarBank@NUS Repository. https://doi.org/10.1111/j.1467-9868.2008.00693.x
dc.identifier.issn13697412
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/105363
dc.description.abstractContemporary 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. © 2008 Royal Statistical Society.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1111/j.1467-9868.2008.00693.x
dc.sourceScopus
dc.subjectBayesian information criterion
dc.subjectDiverging number of parameters
dc.subjectLasso
dc.subjectSmoothly clipped absolute deviation
dc.typeArticle
dc.contributor.departmentSTATISTICS & APPLIED PROBABILITY
dc.description.doi10.1111/j.1467-9868.2008.00693.x
dc.description.sourcetitleJournal of the Royal Statistical Society. Series B: Statistical Methodology
dc.description.volume71
dc.description.issue3
dc.description.page671-683
dc.identifier.isiut000266602200006
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