Weerakkody, Govinda J.; Johnson, Dallas E. Estimation of within model parameters in regression models with a nested error structure. (English) Zbl 0781.62105 J. Am. Stat. Assoc. 87, No. 419, 708-713 (1992). Summary: 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. Cited in 2 Documents MSC: 62J05 Linear regression; mixed models 62F10 Point estimation 62K10 Statistical block designs Keywords:balanced incomplete block design; estimated generalized least squares estimator; hypergeometric functions; restricted randomizations; ordinary least squares estimates; nested error structure; uniformly minimum variance unbiased PDFBibTeX XMLCite \textit{G. J. Weerakkody} and \textit{D. E. Johnson}, J. Am. Stat. Assoc. 87, No. 419, 708--713 (1992; Zbl 0781.62105) Full Text: DOI