Improved Estimators for Ratios of Variance Components
作者:
Wei-Yin Loh,
期刊:
Journal of the American Statistical Association
(Taylor Available online 1986)
卷期:
Volume 81,
issue 395
页码: 699-702
ISSN:0162-1459
年代: 1986
DOI:10.1080/01621459.1986.10478324
出版商: Taylor & Francis Group
关键词: Analysis of variance;Random effects model;Admissibility;Bayes;Maximum likelihood;Restricted maximum likelihood;Noninformative prior
数据来源: Taylor
摘要:
The problem of estimating a ratio of variance components in the balanced one-way random effects model is considered. It is shown that in terms of mean squared error, the ML, REML (or truncated ANOVA), and Bayes modal estimators (using the noninformative prior) are inadmissible. An estimator that dominates all three is derived. Two other estimators that are adaptive in nature are also introduced. The new estimators are shown to possess much-improved mean squared error properties. The results easily extend to balanced higher-way random or mixed effects models.
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