Outlier detection in multivariate time series via projection pursuit

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dc.contributor.author Galeano, Pedro
dc.contributor.author Peña, Daniel
dc.contributor.author Tsay, Ruey S.
dc.date.accessioned 2006-11-09T10:55:53Z
dc.date.available 2006-11-09T10:55:53Z
dc.date.issued 2004-09
dc.identifier.uri http://hdl.handle.net/10016/215
dc.description.abstract This article uses Projection Pursuit methods to develop a procedure for detecting outliers in a multivariate time series. We show that testing for outliers in some projection directions could be more powerful than testing the multivariate series directly. The optimal directions for detecting outliers are found by numerical optimization of the kurtosis coefficient of the projected series. We propose an iterative procedure to detect and handle multiple outliers based on univariate search in these optimal directions. In contrast with the existing methods, the proposed procedure can identify outliers without pre-specifying a vector ARMA model for the data. The good performance of the proposed method is verified in a Monte Carlo study and in a real data analysis.
dc.format.extent 516235 bytes
dc.format.mimetype application/pdf
dc.language.iso eng
dc.language.iso eng
dc.relation.ispartofseries UC3M Working Papers. Statistics and Econometrics
dc.relation.ispartofseries 2004-11
dc.title Outlier detection in multivariate time series via projection pursuit
dc.type workingPaper
dc.subject.eciencia Estadística
dc.rights.accessRights openAccess
dc.identifier.repec ws044211
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