聚集数据线性模型参数的多元聚集改进的广义岭估计的相对效率
The Relative Efficiency of the Improved Generalized Ridge Estimator for Multivariate Aggregation of the Parameters of the Linear Model with Aggregated Data
丁洁 1余新宏 1张海波1
作者信息
- 1. 合肥经济学院基础课教学部,安徽 合肥 230011
- 折叠
摘要
针对聚集数据的复共线性模型,岭估计在参数的稳定性方面过于强调,但参数的无偏性方面被忽视了.利用Stein压缩理论技术,鉴于广义Liu型估计方法之上,给出了多元聚集改进的广义岭估计.研究了该类改进估计的优良性,论证了其中两种相对效率的上界限问题.
Abstract
In this paper,based on Liu-type and Stein-type compression estimation method,an improved general-ized ridge estimation of multivariate aggregation for ill-conditioned aggregated data model is proposed.The superior-ity of the improved generalized ridge estimator for multivariate aggregation is discussed,and the upper bounds of the two relative efficiencies are obtained.
关键词
聚集数据/多元聚集/广义岭估计/相对效率Key words
aggregated date/multivariate aggregation/generalized ridge estimation/relative efficiency引用本文复制引用
出版年
2024
NETL
NSTL
万方数据