Likelihood inference for small variance components

Steven E. Stern*, A. H. Welsh

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    19 Citations (Scopus)


    The authors explore likelihood-based methods for making inferences about the components of variance in a general normal mixed linear model. In particular, they use local asymptotic approximations to construct confidence intervals for the components of variance when the components are close to the boundary of the parameter space. In the process, they explore the question of how to profile the restricted likelihood (REML). Also, they show that general REML estimates are less likely to fall on the boundary of the parameter space than maximum-likelihood estimates and that the likelihood-ratio test based on the local asymptotic approximation has higher power than the likelihood-ratio test based on the usual chi-squared approximation. They examine the finite-sample properties of the proposed intervals by means of a simulation study.

    Original languageEnglish
    Pages (from-to)517-532
    Number of pages16
    JournalCanadian Journal of Statistics
    Issue number3
    Publication statusPublished - Sept 2000


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