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Linear statistical models with constraints revisited. (English) Zbl 0851.62050
Summary: In the practice, two classes of basic linear models with constraints occur, i.e., models with observations of a complete first order vector parameter and models with observations of an incomplete first order vector parameter. In the former class, the locally best linear unbiased estimators are known explicitly. In the latter class, they are given by algorithms.
Explicit formulae for these estimators are given and the effects of constraints on the estimators in the form of suitable projection operators correcting estimators non respecting these constraints are investigated in both of the classes.

MSC:
62J05 Linear regression; mixed models
62F30 Parametric inference under constraints
62H12 Estimation in multivariate analysis
62F10 Point estimation
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References:
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