Covariance structure selection in general mixed models
作者:
Russ Wolfinger,
期刊:
Communications in Statistics - Simulation and Computation
(Taylor Available online 1993)
卷期:
Volume 22,
issue 4
页码: 1079-1106
ISSN:0361-0918
年代: 1993
DOI:10.1080/03610919308813143
出版商: Marcel Dekker, Inc.
关键词: random effects;repeated measures;REML;variance modeling
数据来源: Taylor
摘要:
This article describes a unified approach to variance modeling and inference in the context of a general form of the normal-theory linear mixed model. The primary variance modeling objects are parameterized covari-ance structures, examples being diagonal, compound-symmetry, unstructured, timeseries, and spatial. These structures can enter in two different places in the general mixed model, and the combination of one or both of these places with the variety of structures provides a rich class of variance models. The approach is likelihood-based, and involves the use of both maximum likelihood and restricted maximum likelihood. Two examples provide illustration.
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