A procedure for detecting a pair of outliers in multivariate dataset

dc.contributor.author	Nkansah, B.K.
dc.contributor.author	Gordor, B.K.
dc.date.accessioned	2021-09-03T17:43:42Z
dc.date.available	2021-09-03T17:43:42Z
dc.date.issued	2012
dc.identifier.issn	23105496
dc.identifier.uri	http://hdl.handle.net/123456789/6037
dc.description	9p:, ill.	en_US
dc.description.abstract	The paper presents a procedure for detecting a pair of outliers in multivariate data. The procedure involves a reduction of the dimensionality of the dataset to only two dimensions along outlier displaying components, and then determines the orientation of a least squares ellipse that fts the scatter of points of the two dimensional dataset. Finally, the reduced data is projected unto a vector which is determined in terms of the orientation of the ellipse. The results show that if two observations constitute a pair of outliers in a data set, then the pair is extreme at either ends of the one-dimensional projection and separated clearly from the remaining observations. If the two outliers are not distinct on such a one-dimensional projection, three key rules are prescribed for successful determination of the right pair of outliers	en_US
dc.language.iso	en	en_US
dc.publisher	University of Cape Coast	en_US
dc.subject	Multiple Outlier Detection	en_US
dc.subject	Outlier Displaying Component	en_US
dc.title	A procedure for detecting a pair of outliers in multivariate dataset	en_US
dc.type	Article	en_US