半相依回归模型参数的协方差改进估计  

Improvement on the Covariance Estimation of Parameters in Seemingly Unrelated Regressions

作　　者：马铁丰[1] 王松桂[2] 

机构地区：[1]西南财经大学统计学院,成都610074 [2]北京工业大学应用数理学院,北京100022

出　　处：《工程数学学报》2008年第6期1074-1080,共7页Chinese Journal of Engineering Mathematics

基　　金：国家自然科学基金数学天元青年基金(10726045)

摘　　要：本文研究了两个半相依回归系统的未知回归系数的估计问题。本文首先给出一种基于方差分量限定估计的两步协方差改进估计,并且给出了均方误差意义下优于最小二乘估计的条件。对于基于方差分量非限定估计的两步协方差改进估计,利用服从Wishart分布随机变量的可加性,本文给出了一种全新的估计形式,并且证明了该估计较文献中给出的两步协方差改进估计更加有效。The estimation of regression coefficients in seemingly unrelated regressions with two linear regression models is considered in this paper. First, a covariance adjusted two-stage estimators based on the unrestricted estimate of variance component is obtained. A sufficient condition under which it dominates the least square estimation is derived. A new insight into covariance adjusted two-stage estimators based on the restricted estimate of variance component is discussed. Using the additive property of Wishart distribution variables, a new estimation is also obtained. Our new estimations provide more powerful availability.

关 键 词：半相依模型 协方差改进估计 两步估计 

分 类 号：O212.1[理学—概率论与数理统计]