Influence of incomplete observations in multiple linear regression

Research output: Contribution to journalArticle

1 Scopus citations

Abstract

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
Volume8
Issue number2
DOIs
StatePublished - Jun 1989
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Keywords

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

Fingerprint Dive into the research topics of 'Influence of incomplete observations in multiple linear regression'. Together they form a unique fingerprint.

Cite this