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Estimation of within Model Parameters in Regression Models with a Nested Error Structure

 

作者: GovindaJ. Weerakkody,   DallasE. Johnson,  

 

期刊: Journal of the American Statistical Association  (Taylor Available online 1992)
卷期: Volume 87, issue 419  

页码: 708-713

 

ISSN:0162-1459

 

年代: 1992

 

DOI:10.1080/01621459.1992.10475272

 

出版商: Taylor & Francis Group

 

关键词: Balanced incomplete block design;Estimated generalized least squares estimator;Hypergeometric functions

 

数据来源: Taylor

 

摘要:

Restricted randomizations, similar to those in split-plot type experiments, often are adapted to assign quantitative treatment factors to experimental units. Such restrictions result in the experiment having a nested error structure. Sufficient conditions are presented under which ordinary least squares (OLS) estimates of regressor parameters are uniformly minimum variance unbiased (UMVU). If one designs experiments so that these conditions are satisfied, the analysis is straightforward and easy. When these conditions are not met, three different estimators of nested regressor parameters are suggested and compared.

 

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