首页> 外文期刊>Statistical Methods and Applications >Covariance matrix estimation in a seemingly unrelated regression model under Stein's loss
【24h】

Covariance matrix estimation in a seemingly unrelated regression model under Stein's loss

机译:在斯坦因损失下看似无关的回归模型中的协方差矩阵估计

获取原文
获取原文并翻译 | 示例
       

摘要

A seemingly unrelated regression model has been commonly used for describing a set of different regression models with correlations. This paper discusses the estimation of the covariance matrix in a seemingly unrelated regression model under Stein's loss function. In particular, when the correlation matrix is assumed to be known, a best equivariant estimator of the covariance matrix is derived. Its properties are investigated and a connection to a best equivariant estimator of regression coefficients given in a previous study is shown. Results of numerical simulations and an illustrative example are also presented to compare the best equivariant estimator of the covariance matrix with several conventional covariance matrix estimators.
机译:似乎无关的回归模型通常用于描述一组具有相关性的不同回归模型。本文讨论了斯坦因损失函数下一个看似无关的回归模型中协方差矩阵的估计。特别地,当假定相关矩阵是已知的时,推导协方差矩阵的最佳等方估计量。研究了它的性质,并显示了与先前研究中给出的回归系数的最佳等变估计量的联系。还提供了数值模拟的结果和一个说明性示例,以比较协方差矩阵的最佳等方估计量与几种常规的协方差矩阵估计量。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号