NDLI: Cramer-Rao bounds and Monte Carlo calculation of the Fisher information matrix in difficult problems

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Cramer-Rao bounds and Monte Carlo calculation of the Fisher information matrix in difficult problems

Content Provider	Semantic Scholar
Author	Spall, J. C.
Copyright Year	2004
Abstract	The Fisher information matrix summarizes the amount of information in the data relative to the quantities of interest. There are many applications of the information matrix in modeling, systems analysis, and estimation, including confidence region calculation, input design, prediction bounds, and "noninformative" priors for Bayesian analysis. This paper reviews some basic principles associated with the information matrix, presents a resampling-based method for computing the information matrix together with some new theory related to efficient implementation, and presents some numerical results. The resampling-based method relies on an efficient technique for estimating the Hessian matrix, introduced as part of the adaptive ("second-order") form of the simultaneous perturbation stochastic approximation (SPSA) optimization algorithm.
Starting Page	239
Ending Page	266
Page Count	28
File Format	PDF HTM / HTML
DOI	10.23919/acc.2004.1384392
Volume Number	4
Journal	Proceedings of the 2004 American Control Conference
Alternate Webpage(s)	http://folk.ntnu.no/skoge/prost/proceedings/acc04/Papers/0559_ThM17.1.pdf
Alternate Webpage(s)	http://www.nt.ntnu.no/users/skoge/prost/proceedings/acc04/Papers/0559_ThM17.1.pdf
Alternate Webpage(s)	https://doi.org/10.23919/acc.2004.1384392
Language	English
Access Restriction	Open
Content Type	Text
Resource Type	Article

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