Title :
Constrained hypothesis testing and the Cramér-Rao bound
Author :
Moore, Terrence J. ; Sadler, Brian M.
Author_Institution :
Army Res. Lab., Adelphi, MD, USA
Abstract :
The classical Wald and Rao test statistics are asymptotically equivalent to the generalized likelihood ratio test statistics, while not requiring parameter estimation under both hypotheses, and so they provide lower complexity test statistics. In this paper we develop corresponding variations of the Wald and Rao test for nested hypothesis testing under parameter constraints. The resulting tests incorporate the constrained Cramér-Rao bound formulation from Stoica and Ng, and unify some asymptotic hypothesis testing results. Examples will illustrate key ideas and test performance.
Keywords :
maximum likelihood estimation; signal processing; Cramer-Rao bound; asymptotic hypothesis testing; constrained hypothesis testing; generalized likelihood ratio test statistic; Biological system modeling; Jacobian matrices; Mathematical model; Maximum likelihood estimation; Signal to noise ratio; Testing; Training; Hypothesis testing; asymptotic analysis; constrained Cramér-Rao bound;
Conference_Titel :
Sensor Array and Multichannel Signal Processing Workshop (SAM), 2010 IEEE
Conference_Location :
Jerusalem
Print_ISBN :
978-1-4244-8978-7
Electronic_ISBN :
1551-2282
DOI :
10.1109/SAM.2010.5606713