Nonnegative Minimum Biased Quadratic Estimation in the Linear Regression Models
In the paper the problem of nonnegative estimation of [beta]'H[beta] + h[sigma]2 in the linear model E(y) = X[beta], Var(y)= [sigma]2I is discussed. Here H is a nonnegative definite matrix while h is a nonnegative scalar. An iterative procedure for the nonnegative minimum biased quadratic estimator is described. Moreover, in the case that H and X'X commute, an explicit formula for this estimator is given. Admissibility of the estimator is proved. The results are applied to nonnegative estimation of the total mean squared error of a linear biased estimator.
Year of publication: |
1995
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Authors: | Gnot, S. ; Trenkler, G. ; Zmyslony, R. |
Published in: |
Journal of Multivariate Analysis. - Elsevier, ISSN 0047-259X. - Vol. 54.1995, 1, p. 113-125
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Publisher: |
Elsevier |
Saved in:
Online Resource
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