Procedures for the identification of multiple influential observations in linear regression

Author's Department

Mathematics & Actuarial Science Department

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https://doi.org/10.1080/02664763.2013.868418

Document Type

Research Article

Publication Title

Journal of Applied Statistics

Publication Date

1-1-2014

doi

10.1080/02664763.2013.868418

Abstract

Since the seminal paper by Cook (1977) in which he introduced Cook's distance, the identification of influential observations has received a great deal of interest and extensive investigation in linear regression. It is well documented that most of the popular diagnostic measures that are based on single-case deletion can mislead the analysis in the presence of multiple influential observations because of the well-known masking and/or swamping phenomena. Atkinson (1981) proposed a modification of Cook's distance. In this paper we propose a further modification of the Cook's distance for the identification of a single influential observation. We then propose new measures for the identification of multiple influential observations, which are not affected by the masking and swamping problems. The efficiency of the new statistics is presented through several well-known data sets and a simulation study. © 2013 Taylor & Francis.

First Page

1315

Last Page

1331

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