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Constrained linear discriminant rule via the Studentized classification statistic based on monotone missing data
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Mathematical Statistics.
2012 (English)In: SUT Journal of Mathematics, ISSN 0916-5746, Vol. 48, no 1, 55-69 p.Article in journal (Refereed) Published
Abstract [en]

This paper provides an asymptotic expansion for the distribution of the Studentized linear discriminant function with k-step monotone missing training data. It turns out to be a certain generalization of the results derived by Anderson [1] and Shutoh and Seo [12]. Furthermore we also derive the cutoff point constrained by a conditional probability of misclassification using the idea of McLachlan [8]. Finally we perform Monte Carlo simulation to evaluate our results.

Place, publisher, year, edition, pages
2012. Vol. 48, no 1, 55-69 p.
Keyword [en]
Asymptotic expansion, Linear discriminant analysis, Monotone missing data, Probabilities of misclassification
National Category
Mathematics
Identifiers
URN: urn:nbn:se:kth:diva-129794Scopus ID: 2-s2.0-84879267361OAI: oai:DiVA.org:kth-129794DiVA: diva2:654637
Note

QC 20131008

Available from: 2013-10-08 Created: 2013-10-04 Last updated: 2014-05-09Bibliographically approved

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CiteExportLink to record
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