The detection of errors in multivariate data using principal components

Douglas M. Hawkins

Research output: Contribution to journalArticlepeer-review

56 Scopus citations

Abstract

Serious problems arise in the maintenance of reliability in large data bases, since it becomes difficult to verify incoming data manually. This article considers the case in which the base consists of data vectors following a multivariate normal distribution. Five screening procedures are proposed—a “one-at-a-time” test, the standard χ2test, and three statistics derived from principal component analysis. From analysis of a practical example, it emerges that the statistics derived from principal component analysis have superior performance. © 1974, Taylor & Francis Group, LLC.

Original languageEnglish (US)
Pages (from-to)340-344
Number of pages5
JournalJournal of the American Statistical Association
Volume69
Issue number346
DOIs
StatePublished - 1974

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