ASSESSING INFLUENCE IN MULTIPLE LINEAR REGRESSION WITH INCOMPLETE DATA.

Weichung J. Shih, Sanford Weisberg

Research output: Contribution to journalArticlepeer-review

26 Scopus citations

Abstract

The problem of assessing influence and detecting influential cases in multiple linear regression with incomplete data is considered. A case is said to be influential if appreciable changes in fitted regression coefficients occur when it is removed from the data. A one-step influence measure is derived, based on the EM algorithm for detecting cases that are influential in the maximum likelihood estimation of the regression coefficients. Results are compared with the (complete data) Cook’s distance measure. Techniques are demonstrated by examples.

Original languageEnglish (US)
Pages (from-to)231-239
Number of pages9
JournalTechnometrics
Volume28
Issue number3
DOIs
StatePublished - Aug 1986

Keywords

  • Cook’s distance
  • Diagnostics

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