Title :
The value of information in constrained parametric models
Author :
Moore, Terrence J. ; Sadler, Brian M.
Author_Institution :
Army Res. Lab., Adelphi, MD, USA
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;
Conference_Titel :
Statistical Signal Processing Workshop (SSP), 2012 IEEE
Conference_Location :
Ann Arbor, MI
Print_ISBN :
978-1-4673-0182-4
Electronic_ISBN :
pending
DOI :
10.1109/SSP.2012.6319685
