 Accuracy estimation for quasi-Monte Carlo simulationsWilliam C Snyder Mathematics and Computers in Simulation (MATCOM), 2000, vol. 54, issue 1, 131-143 Abstract: The conventional Monte Carlo approach to integration and simulation is a useful alternative to analytic or quadrature methods. It has been recognized through theory and practice that a variety of uniformly distributed sequences provide more accurate results than a purely pseudorandom sequence. The improvement in accuracy depends on the number of dimensions and the discrepancy of the sequence, which are known, and the variation of the function, which is often not known. Unlike pseudorandom methods, the accuracy of a quasirandom simulation cannot be estimated using the sample variance of the evaluations or by bootstrapping. The improvement in time-to-accuracy using quasirandom methods can be as large as several orders of magnitude, so even an empirical accuracy estimator is worth pursuing. In this paper, we discuss several methods for quasirandom empirical accuracy estimation and evaluate a modified empirical technique that appears to be useful. Keywords: Monte Carlo method; Quasi-Monte Carlo method; Uniformly distributed sequences; Low-discrepancy sequences; Numerical integration; Accuracy assessment (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:54:y:2000:i:1:p:131-143 Access Statistics for this article Mathematics and Computers in Simulation (MATCOM) is currently edited by Robert Beauwens More articles in Mathematics and Computers in Simulation (MATCOM) from Elsevier |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.