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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