DocumentCode
3540479
Title
The value of information in constrained parametric models
Author
Moore, Terrence J. ; Sadler, Brian M.
Author_Institution
Army Res. Lab., Adelphi, MD, USA
fYear
2012
fDate
5-8 Aug. 2012
Firstpage
293
Lastpage
296
Abstract
We consider the value of information introduced through constraints on the parameters, by showing the Fisher information gain and the resulting Cramér-Rao bound (CRB) reduction. In doing so, we show that the constrained CRB (CCRB) can be found by a transformation of parameters or a projection onto the tangent of the constraint space. Finally, we determine the characteristics of optimal constraints that minimize the trace of the CCRB, thereby attaining the lowest bound on the sum of mean-square errors of unbiased estimators.
Keywords
information theory; least mean squares methods; Cramer-Rao bound reduction; Fisher information gain; constrained parametric models; mean-square errors; unbiased estimators; Eigenvalues and eigenfunctions; Jacobian matrices; Maximum likelihood estimation; Mean square error methods; Parametric statistics; Vectors; CCRB; CRB; Fisher information; equality constraints; parametric models; value of information;
fLanguage
English
Publisher
ieee
Conference_Titel
Statistical Signal Processing Workshop (SSP), 2012 IEEE
Conference_Location
Ann Arbor, MI
ISSN
pending
Print_ISBN
978-1-4673-0182-4
Electronic_ISBN
pending
Type
conf
DOI
10.1109/SSP.2012.6319685
Filename
6319685
Link To Document