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Article: Shrinkage-based diagonal discriminant analysis and its applications in high-dimensional data

TitleShrinkage-based diagonal discriminant analysis and its applications in high-dimensional data
Authors
Issue Date2009
Citation
Biometrics, 2009, v. 65 n. 4, p. 1021-1029 How to Cite?
AbstractHigh-dimensional data such as microarrays have brought us new statistical challenges. For example, using a large number of genes to classify samples based on a small number of microarrays remains a difficult problem. Diagonal discriminant analysis, support vector machines, and k-nearest neighbor have been suggested as among the best methods for small sample size situations, but none was found to be superior to others. In this article, we propose an improved diagonal discriminant approach through shrinkage and regularization of the variances. The performance of our new approach along with the existing methods is studied through simulations and applications to real data. These studies show that the proposed shrinkage-based and regularization diagonal discriminant methods have lower misclassification rates than existing methods in many cases. © 2009, The International Biometric Society.
Persistent Identifierhttp://hdl.handle.net/10722/194255
ISSN
2015 Impact Factor: 1.36
2015 SCImago Journal Rankings: 1.906
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorPang, H-
dc.contributor.authorTong, T-
dc.contributor.authorZhao, H-
dc.date.accessioned2014-01-30T03:32:21Z-
dc.date.available2014-01-30T03:32:21Z-
dc.date.issued2009-
dc.identifier.citationBiometrics, 2009, v. 65 n. 4, p. 1021-1029-
dc.identifier.issn0006-341X-
dc.identifier.urihttp://hdl.handle.net/10722/194255-
dc.description.abstractHigh-dimensional data such as microarrays have brought us new statistical challenges. For example, using a large number of genes to classify samples based on a small number of microarrays remains a difficult problem. Diagonal discriminant analysis, support vector machines, and k-nearest neighbor have been suggested as among the best methods for small sample size situations, but none was found to be superior to others. In this article, we propose an improved diagonal discriminant approach through shrinkage and regularization of the variances. The performance of our new approach along with the existing methods is studied through simulations and applications to real data. These studies show that the proposed shrinkage-based and regularization diagonal discriminant methods have lower misclassification rates than existing methods in many cases. © 2009, The International Biometric Society.-
dc.languageeng-
dc.relation.ispartofBiometrics-
dc.titleShrinkage-based diagonal discriminant analysis and its applications in high-dimensional data-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1111/j.1541-0420.2009.01200.x-
dc.identifier.pmid19302409-
dc.identifier.scopuseid_2-s2.0-70450228445-
dc.identifier.volume65-
dc.identifier.issue4-
dc.identifier.spage1021-
dc.identifier.epage1029-
dc.identifier.isiWOS:000272128200003-

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