Identification and classification of multiple outliers, high leverage points and influential observations in linear regression. Issue 3 (17th February 2016)

• Record Type: Journal Article
• Authors: Nurunnabi, A.A.M.; Nasser, M.; Imon, A.H.M.R.
• Is Part Of: Journal of applied statistics. Volume 43:Issue 3(2016)
• Page Start: 509
• Page End: 525
• Publication Date: 2016-02-17
• Subjects: generalized residual -- group deletion -- influence distance -- leverage matrix -- LRI plot -- Mahalanobis distance -- masking -- outlier -- regression diagnostics -- robust regression; Statistics -- Periodicals; 519.5
• DOI: 10.1080/02664763.2015.1070806
• Languages: English
• ISSNs: 0266-4763

Abstract

Abstract : Detection of multiple unusual observations such as outliers, high leverage points and influential observations (IOs) in regression is still a challenging task for statisticians due to the well-known masking and swamping effects. In this paper we introduce a robust influence distance that can identify multiple IOs, and propose a sixfold plotting technique based on the well-known group deletion approach to classify regular observations, outliers, high leverage points and IOs simultaneously in linear regression. Experiments through several well-referred data sets and simulation studies demonstrate that the proposed algorithm performs successfully in the presence of multiple unusual observations and can avoid masking and/or swamping effects.