Likelihood inference for small variance components

Steven E. Stern, A. H. Welsh

Research output: Contribution to journalArticleResearchpeer-review

16 Citations (Scopus)

Abstract

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
Volume28
Issue number3
DOIs
Publication statusPublished - Sep 2000
Externally publishedYes

Fingerprint

Restricted Maximum Likelihood
Components of Variance
Likelihood Inference
Local Approximation
Variance Components
Asymptotic Approximation
Likelihood Ratio Test
Parameter Space
Likelihood
Mixed Linear Model
Chi-squared
Maximum Likelihood Estimate
High Power
Confidence interval
Likely
Simulation Study
Interval
Approximation
Estimate
Inference

Cite this

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Likelihood inference for small variance components. / Stern, Steven E.; Welsh, A. H.

In: Canadian Journal of Statistics, Vol. 28, No. 3, 09.2000, p. 517-532.

Research output: Contribution to journalArticleResearchpeer-review

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