Abstract
In a Gauss-Markov model with linear restrictions, two estir mators of the vector parameters are compared with respect to the matrix risk function and with respect to a weighted quadratic risk function. The first from the competitory estimators is based on the sample and linear restrictions contained in the model, while the second estimator additionally utilizes some linear restrictions which are not necessary fulfilled by the vector parameters. The criteria of the superiority of one of the estimators over the other are expressed by the conditions on parameters of a well defined probability density function.
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© 1985 Springer-Verlag Berlin Heidelberg
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Kłaczyński, K., Pordzik, P. (1985). On Improving Estimation in a Restricted Gauss-Markov Model. In: Caliński, T., Klonecki, W. (eds) Linear Statistical Inference. Lecture Notes in Statistics, vol 35. Springer, New York, NY. https://doi.org/10.1007/978-1-4615-7353-1_13
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DOI: https://doi.org/10.1007/978-1-4615-7353-1_13
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