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Best Linear Unbiased Estimation in Mixed Models of the Analysis of Variance
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The article was published on 1984-12-01 and is currently open access. It has received 3 citations till now. The article focuses on the topics: Mixed model.read more
Citations
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The mixed-model ANOVA: the truth, the computer packages, the books. Part I: balanced data
TL;DR: The analysis of variance of mixed models is fraught with potential pitfalls and some computer packages still report incorrect results for the balanced model and some textbooks gloss over or ignore some of these pitfalls.
A bibliography on variance components an introduction and an update: 1984-2002
Hardeo Sahai,Anwer Khurshid +1 more
TL;DR: In particular, the study of variance through a class of linear models known as random and mixed models is a central topic in statistics with wide ramifications in both theory and applications as discussed by the authors.
Journal ArticleDOI
The Matrix Handling of BLUE and BLUP in the Mixed Linear Model
TL;DR: The mixed model of analysis of variance is a linear model in which some terms that would otherwise be unknown constants are, in fact, unobservable realizations of random variables as mentioned in this paper.
References
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Journal ArticleDOI
On Canonical Forms, Non-Negative Covariance Matrices and Best and Simple Least Squares Linear Estimators in Linear Models
TL;DR: In this paper, it was shown that a linear function is blue for its expectation if and only if the column space of the function is equal to the column of the column for the function.
Journal ArticleDOI
A Necessary and Sufficient Condition That Ordinary Least-Squares Estimators Be Best Linear Unbiased
TL;DR: In this article, it was shown that in a standard linear regression model, ordinary least-squares estimators are best linear unbiased if and only if the errors have the same variance and the same nonnegative coefficient of correlation between each pair.
Journal ArticleDOI
Optimal testing for fixed effects in general balanced mixed classification models
TL;DR: In this article, unbiased tests for linear hypotheses about fixed effects in general balanced normal mixed classification models are considered and in complete families similar ANOVA tests are shown to be uniformly most powerful invariant unbiased.
Journal ArticleDOI
Maximum likelihood analysis of the mixed model: the balanced case
David W. Smith,R.R. Hocking +1 more
TL;DR: In this paper, the inverse of the covariance matrix for the mixed analysis-of-variance model with balanced data is developed, analytically, for the identification of minimal sufficient statistics and in developing the likelihood equations.