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The Asymptotic Distribution of REML Estimators

Authors
Journal
Journal of Multivariate Analysis
0047-259X
Publisher
Elsevier
Publication Date
Volume
45
Issue
2
Identifiers
DOI: 10.1006/jmva.1993.1034
Disciplines
  • Mathematics

Abstract

Abstract Restricted maximum likelihood (REML) estimation is a method employed to estimate variance-covariance parameters from data that follow a Gaussian linear model. In applications, it has either been conjectured or assumed that REML estimators are asymptotically Gaussian with zero mean and variance matrix equal to the inverse of the restricted information matrix. In this article, we give conditions under which the conjecture is true and apply our results to variance-components models. An important application of variance components is to census undercount; a simulation is carried out to verify REML′s properties for a typical census undercount model.

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