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

Mihoko Minami, Kunio Shimizu

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

The maximum likelihood estimate and the restricted or residual maximum likelihood estimate are considered for a common intraclass correlation coefficient among several bivariate normal distributions when some observations on either of the variables are missing. The estimates are given as the solutions of polynomial equations. Asymptotic variances of both estimates are obtained from the corresponding information matrices. The variance stabilizing transformation, which can be used to perform hypothesis tests and construct a confidence interval for ρ, is derived. Copyright

Original languageEnglish
Pages (from-to)3-14
Number of pages12
JournalAmerican Journal of Mathematical and Management Sciences
Volume17
Issue number1-2
Publication statusPublished - 1997
Externally publishedYes

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Intraclass Correlation
Bivariate Normal Distribution
Missing Observations
Normal distribution
Maximum Likelihood Estimate
Maximum likelihood
Residual Maximum Likelihood
Intraclass Correlation Coefficient
Restricted Maximum Likelihood
Information Matrix
Hypothesis Test
Polynomial equation
Asymptotic Variance
Estimate
Confidence interval
Polynomials
Observation

Keywords

  • Asymptotic variance
  • Fisher information matrix
  • MLE
  • REMLE
  • Variance stabilizing transformation

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering
  • Applied Mathematics

Cite this

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