Nonnegative Minimum Biased Quadratic Estimation in the Linear Regression Models

S. Gnot, G. Trenkler and Roman Zmyślony ()

Journal of Multivariate Analysis, 1995, vol. 54, issue 1, 113-125

Abstract: 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.

Date: 1995
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