Estimation for a common correlation coefficient in bivariate normal distributions with missing observations

Mihoko Minami, Kunio Shimizu

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

The maximum likelihood (ML) estimate and the restricted or residual maximum likelihood (REML) estimate are considered for a common correlation coefficient among several bivariate normal distributions with different variances when some observations on either of the variables are missing. The use of incomplete data in ML and REML estimation reduces mean squared errors of the correlation estimates. Reduction is large when the absolute value of a common correlation is large or numbers of paired observations are small. An example and some simulation results are given to illustrate the characteristics of the estimates.

Original languageEnglish
Pages (from-to)1136-1146
Number of pages11
JournalBiometrics
Volume54
Issue number3
DOIs
Publication statusPublished - 1998 Sep 1
Externally publishedYes

Keywords

  • Asymptotic variance
  • Fisher information matrix
  • Maximum likelihood estimate
  • Re-stricted maximum likelihood estimate
  • Variance-stabilizing transformation

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Agricultural and Biological Sciences(all)
  • Applied Mathematics

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