A Jackknife Method for Estimation of Variance Components
作者:
Christian Lavergne,
期刊:
Statistics
(Taylor Available online 1995)
卷期:
Volume 27,
issue 1-2
页码: 1-13
ISSN:0233-1888
年代: 1995
DOI:10.1080/02331889508802506
出版商: Gordon & Breach Science Publishers
关键词: AMS classification;Primary 62J05;62F10;secondary 62G09;random effect linear models;Jackknife estimators;MINQUE
数据来源: Taylor
摘要:
This paper concerns a method of estimation of variance components in a random effect linear model. It is mainly a resampling method and relies on the Jackknife principle. The derived estimators are presented as least squares estimators in an appropriate linear model, and one of them appears as a MINQUE (Minimum Norm Quadratic Unbiased Estimation) estimator. Our resampling method is illustrated by an example given by C. R. Rao [7] and some optimal properties of our estimator are derived for this example. In the last part, this method is used to derive an estimation of variance components in a random effect linear model when one of the components is assumed to be known.
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