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minimum mean square error estimation
In many cases we can consider the regression parameters as realizations of a random variable. In these situations the minimum mean square error estimator seems to be useful and important. The explicit form of this estimator is given in the case that both the covariance matrices of the random parameters and those of the error vector are singular.
[1] C. R. Rao S. K. Mitra: Generalized inverse of matrices and its applications. John Wiley, New York 1971. MR 0338013
[2] J. S. Chipman: On least squares with insufficient observations. J. Amer. Statist. Assoc. 59 (1964), 1078-1111. DOI 10.1080/01621459.1964.10480751 | MR 0175220 | Zbl 0144.42401
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