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Likelihood inference for small variance components

Stern, S.E. and Welsh, A.H. (2000) Likelihood inference for small variance components The Canadian Journal of Statistics, 28, pp. 517-532.

Record type: Article


In this paper, we develop likelihood-based methods for making inferences about the components of variance in a general normal mixed linear model. In particular, we 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, we explore the question of how to profile the restricted likelihood (REML), show that general REML estimates have a lower probability of being on the boundary than maximum likelihood estimates, and show that the likelihood-ratio test based on the local asymptotic approximation has higher power against local alternatives than the likelihood-ratio test based on the usual chi-squared approximation. We explore the finite sample properties of the proposed intervals by means of a small simulation study.

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Published date: 2000
Organisations: Statistics


Local EPrints ID: 29929
PURE UUID: 6a632ec2-3977-455c-aabd-2b539b148ff6

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Date deposited: 16 Mar 2007
Last modified: 17 Jul 2017 15:56

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Author: S.E. Stern
Author: A.H. Welsh

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