Influence of incomplete observations in multiple linear regression

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This paper is concerned with the influence of incomplete data due to random missing values in the multiple linear regression problem. Using the idea of Hampel's influence function, a partial influence function is derived and shown to be useful in several indications. Comparisons with the complete data situation and with the empirical case-deletion distance measure are also given.

Original languageEnglish (US)
Pages (from-to)171-174
Number of pages4
JournalStatistics and Probability Letters
Issue number2
StatePublished - Jun 1989
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


  • EM algorithm
  • influence function
  • missing values
  • multiple linear regression

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