NONNEGATIVE MINIMUM BIASED QUADRATIC ESTIMATION IN THE LINEAR-REGRESSION MODELS

JOURNAL OF MULTIVARIATE ANALYSIS

卷:54

Article

GNOT, S ; TRENKLER, G ; ZMYSLONY, R

关键词: LINEAR MODEL;    NONNEGATIVE MINIMUM BIASED ESTIMATOR;    ADMISSIBILITY;    TOTAL MEAN SQUARED ERROR;    ONE-WAY CLASSIFICATION MODEL;   

DOI  :  10.1006/jmva.1995.1047

来源: Elsevier

【 摘 要 】

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(2)I 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. (C) 1995 Academic Press, Inc.

【 授权许可】

Free