您当前所在位置: 首页 > 学者

朱允民

  • 13浏览

  • 0点赞

  • 0收藏

  • 0分享

  • 116下载

  • 0评论

  • 引用

期刊论文

Minimum Variance in Biased Estimation With Singular Fisher Information Matrix

朱允民Enbin Song Yunmin Zhu Jie Zhou and Zhisheng You

IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL.57, NO.1, JANUARY 2009,-0001,():

URL:

摘要/描述

This paper extends the work of Y. C. Eldar, "Minimum variance in biased estimation: Bounds and asymptotically optimal estimators," in IEEE Trans. Signal Process., vol. 52, pp. 1915-1929, Jul. 2004, which deals with only nonsingular Fisher information matrix. In order to guarantee the uniform Cramér–Rao bound to be a finite lower bound and also to have a feasible solution to the optimization problem in the work of Y. C. Eldar, it is proved that the norms of bias gradient matrices of all biased estimators must have a nonzero exact lower bound, which mainly depends on the rank of the singular Fisher information matrix. The smaller the rank of the singular Fisher information matrix is, the larger the lower bound of norms of bias gradient matrices of all biased estimators is. For a specific Frobenius norm, the exact lower bound is simply the difference between the parameter dimension and the rank of the singular Fisher information matrix.

版权说明:以下全部内容由朱允民上传于   2009年06月29日 15时40分39秒,版权归本人所有。

我要评论

全部评论 0

本学者其他成果

    同领域成果