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Recursive improvement of estimates in a Gauss‐Markov model with linear restrictions
Author(s) -
Kala Radoslaw,
Kłaczyński Krzysztof
Publication year - 1988
Publication title -
canadian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.804
H-Index - 51
eISSN - 1708-945X
pISSN - 0319-5724
DOI - 10.2307/3314736
Subject(s) - estimator , mathematics , gauss , markov chain , simple (philosophy) , markov model , linear model , set (abstract data type) , mathematical optimization , markov process , statistics , computer science , physics , quantum mechanics , philosophy , epistemology , programming language
The minimum‐dispersion linear unbiased estimator of a set of estimable functions in a general Gauss‐Markov model with double linear restrictions is considered. The attention is focused on developing a recursive formula in which an initial estimator, obtained from the unrestricted model, is corrected with respect to the restrictions successively incorporated into the model. The established formula generalizes known results developed for the simple Gauss‐Markov model.