Identification and classification of multiple outliers,high leverage points and influential observations in linear regression |
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Authors: | A.A.M. Nurunnabi M. Nasser A.H.M.R. Imon |
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Affiliation: | 1. SLG, Department of Statistics, University of Rajshahi, Rajshahi, Bangladesh;2. Department of Statistics, University of Rajshahi, Rajshahi, Bangladesh;3. Department of Mathematical Sciences, Ball State University, Muncie, IN, USA |
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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. |
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Keywords: | generalized residual group deletion influence distance leverage matrix LRI plot Mahalanobis distance masking outlier regression diagnostics robust regression |
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