Characterizations of an empirical influence function for detecting influential cases in regression

R. Dennis Cook, Sanford Weisberg

Research output: Contribution to journalArticlepeer-review

144 Scopus citations

Abstract

Traditionally, most of the effort in fitting full rank linear regression models has centered on the study of the presence, strength and form of relationships between the measured variables. As is now well known, least squares regression computations can be strongly influenced by a few cases, and a fitted model may more accurately reflect unusual features of those cases than the overall relationships between the variables. It is of interest, therefore, for an analyst to be able to find influential cases and, based on them, make decisions concerning their usefulness in a problem at hand. Based on an empirical influence function, we discuss methodologies for assessing the influence of individual or groups of cases on a regression problem. We conclude with an example using data from the Florida Area Cumulus Experiments (FACE) on cloud seeding. © 1980 Taylor & Francis Group, LLC.

Original languageEnglish (US)
Pages (from-to)495-508
Number of pages14
JournalTechnometrics
Volume22
Issue number4
DOIs
StatePublished - 1980

Keywords

  • Cloud seeding
  • Distance measures
  • Linear models
  • Outlier tests
  • Residual plotting
  • Robustness

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