Monte Carlo Computation of the Fisher Information Matrix in Nonstandard Settings

James C Spall
<span title="">2005</span> <i title="Informa UK Limited"> <a target="_blank" rel="noopener" href="" style="color: black;">Journal of Computational And Graphical Statistics</a> </i> &nbsp;
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 article reviews some basic principles associated with the information matrix, presents a resamplingbased method for computing the information matrix
more &raquo; ... ogether 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.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="">doi:10.1198/106186005x78800</a> <a target="_blank" rel="external noopener" href="">fatcat:do6tcuxjujdfxhhd73inwjx2bq</a> </span>
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