Best Quadratic Unbiased Prediction in a General Linear Model with Stochastic Regression Coefficients

被引：0

作者：

Liu, Xu-Qing ^{[1
]}

Wu, Yan-Dong ^{[1
]}

Rong, Jian-Ying ^{[2
]}

机构：

[1] Huaiyin Inst Technol, Fac Math & Phys, Huaian 223001, Peoples R China

[2] Huaian Coll Informat Technol, Dept Fdn Courses, Huaian, Peoples R China

来源：

COMMUNICATIONS IN STATISTICS-THEORY AND METHODS | 2011年 / 40卷 / 08期

关键词：

Best quadratic unbiased predictor; Permutation matrix; Quadratic predictability; Stochastic regression coefficient; SUFFICIENCY; ADMISSIBILITY; COMPLETENESS;

D O I：

10.1080/03610921003606327

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

In this article, we discuss on how to predict a combined quadratic parametric function of the form beta' H beta + h sigma(2) in a general linear model with stochastic regression coefficients denoted by y = X beta + e. Firstly, the quadratic predictability of beta' H beta + h sigma(2) is investigated to obtain a quadratic unbiased predictor (QUP) via a general method of structuring an unbiased estimator. This QUP is also optimal in some situations and therefore we hope it will be a fine predictor. To show this idea, we apply the Lagrange multipliers method to this problem and finally reach the expected conclusion through permutation matrix techniques.

引用

页码：1418 / 1433

页数：16

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