Estimation of the mean when data contain non-ignorable missing values from a random effects model

Weichung J. Shih, Hui Quan, Myron N. Chang

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

This paper is concerned with the inference of incomplete data when the missing data process is non-ignorable in the sense of Rubin (Biometrica 38 (1982) 963-974). With the random effects model and the proposed missing data process, the conditions missing at random (MAR) and distinct parameters (DP) are discussed. The impact of the missing data is illustrated by the asymptotic bias of the sample mean based on only the observed data and ignoring the missing data process. Maximum likelihood and moment estimators of the marginal mean are obtained.

Original languageEnglish (US)
Pages (from-to)249-257
Number of pages9
JournalStatistics and Probability Letters
Volume19
Issue number3
DOIs
StatePublished - Feb 22 1994
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Keywords

  • ECM algorithm
  • EM algorithm
  • Missing values
  • distinct parameters
  • missing at random
  • non-ignorable missing data process
  • random effects model

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