Note: On the Value of Function Evaluation Location Information in Monte Carlo Simulation

Thomas J. DiCiccio and Peter W. Glynn
Additional contact information
Thomas J. DiCiccio: Department of Statistics, Stanford University, Stanford, California 94305
Peter W. Glynn: Department of Operations Research, Stanford University, Stanford, California 94305

Management Science, 1995, vol. 41, issue 4, 733-737

Abstract: The point estimator used in naive Monte Carlo sampling weights all the computed function evaluations equally, and it does not take into account the precise locations at which the function evaluations are made. In this note, we consider one-dimensional integration problems in which the integrand is twice continuously differentiable. It is shown that if the weights are suitably modified to reflect the location information present in the sample, then the convergence rate of the Monte Carlo estimator can be dramatically improved from order n -1/2 to order n -2 , where n is the number of function evaluations computed.

Keywords: simulation; Monte Carlo methods; numerical integration (search for similar items in EconPapers)
Date: 1995
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://dx.doi.org/10.1287/mnsc.41.4.733 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:ormnsc:v:41:y:1995:i:4:p:733-737

Access Statistics for this article

More articles in Management Science from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().