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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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