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, 517-532.

Download

[img]
Preview
PDF - Pre print
Download (201Kb)

Description/Abstract

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.

Item Type: Article
Subjects: H Social Sciences > HA Statistics
Divisions: University Structure - Pre August 2011 > School of Mathematics > Statistics
ePrint ID: 29929
Date Deposited: 16 Mar 2007
Last Modified: 27 Mar 2014 18:18
URI: http://eprints.soton.ac.uk/id/eprint/29929

Actions (login required)

View Item View Item

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics