The identi\fcation of outliers in data has been an area of a great deal of attention
\nfor many years. The outlier detection procedure is more cumbersome in regression
\nwhere outliers may occur in the response variable or in the explanatory variables
\nor both. A variety of diagnostic methods are now being used for the identi\fcation
\nof di\u000berent types of outliers in regression. These methods, however, are successful
\nonly if the data set contains a single outlier. In the presence of multiple outliers
\ndiagnostic methods often fail to detect the outliers. This is due to the well-known
\nproblems of masking and swamping e\u000bects. On the other hand the robust methods
\ncan identify the outliers correctly but they are too prone to declare observations
\nto be outlier which is not also desired. In this paper we discuss an approach which
\nis a compromise between these two approaches. We call this approach diagnosticrobust
\napproach where the suspect outliers are identi\fed \frst by robust methods
\nand diagnostic methods are applied later to con\frm the suspicion. We consider
\nseveral well-known data sets to investigate the performance of the diagnosticrobust
\napproach in the detection of outliers in regression.<\/p>\n
Fulltext<\/p>\n","protected":false},"excerpt":{"rendered":"
The identi\fcation of outliers in data has been an area of a great deal of attention for many years. The outlier detection procedure is more cumbersome in regression where outliers may occur in the response variable or in the explanatory variables or both. A variety of diagnostic methods are now being used for the identi\fcation […]<\/p>\n","protected":false},"author":2,"featured_media":0,"menu_order":0,"comment_status":"closed","ping_status":"open","template":"","format":"standard","meta":{"_mi_skip_tracking":false,"_exactmetrics_sitenote_active":false,"_exactmetrics_sitenote_note":"","_exactmetrics_sitenote_category":0,"footnotes":""},"issuem_issue":[15],"issuem_issue_categories":[],"issuem_issue_tags":[],"yoast_head":"\n