Estimation strategies in the presence of nuisance parameters

被引：14

作者：

Gini, F

机构：

[1] Dept. of Information Engineering, University of Pisa, I-56126 Pisa

来源：

SIGNAL PROCESSING | 1996年 / 55卷 / 02期

关键词：

nuisance parameters; Cramer-Rao lower bound; mean square error; ML estimate;

D O I：

10.1016/S0165-1684(97)88179-0

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Suppose that the observed data depend both on certain parameters we want to estimate and on other parameters we do not care about. In other words, there are both wanted and unwanted parameters. Assuming that the latter are known, should we exploit this information to design good estimators for the former? From an example we work out in this paper, it appears that the answer depends on whether we restrict ourselves to unbiased estimators or, vice versa, we also allow biased estimators. Exploiting knowledge of unwanted parameters is recommended with unbiased estimators. With biased estimators, instead, this may not be the winning strategy.

引用

页码：241 / 245

页数：5

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