摘要
对于多元正态线性模型 ,采用极小化均方误差的方法得到了回归系数的一种非线性有偏估计 ,即多元广义Stein估计 ,给出了它的偏差及其均方误差的渐近展开式。在均方误差意义下 ,当误差干扰充分小 (σ→ 0 )时 ,得到了该估计优于 L
For multivariate normal linear model, a nonlinear biased estimate of regression coefficients, which is multivariate generalized Stein estimate, is presented by the method of minimizing mean square error of linear estimate in this paper. Its asymptotic expansions of bias and mean square error are derived, and when error disturbance is sufficiently small, the asymptotic necessary and sufficient conditions are also shown for this estimate to dominate the best linear unbiased estimate under the mean square error criterion.
出处
《武汉理工大学学报》
EI
CAS
CSCD
2001年第9期87-89,共3页
Journal of Wuhan University of Technology
关键词
多元线性模型
均方误差
多元广义
STEIN估计
偏差
回归分析
multivariate linear model
mean square error
multivariate generalized Stein estimate
least squares estimate
asymptotic property
