In a first part of the paper a simulation method for a strictly stationary non-Gaussian process with given one-dimensional marginal distribution (or N-first statistical moments) and autocorrelation function is recalled. This method was already widely treated in the articles [14] and [13]. The objective of the present paper is twofold: first, to simplify this method - if by Mehler formula it is possible to find an autocorrelation function yielding the target autocorrelation function, and second, analyze the difference between the given autocorrelation function and the model one.
Show Summary Details
More options …
More options …
Monte Carlo Methods and Applications
Managing Editor: Sabelfeld, Karl K.
Editorial Board: Binder, Kurt / Bouleau, Nicolas / Chorin, Alexandre J. / Dimov, Ivan / Dubus, Alain / Egorov, Alexander D. / Ermakov, Sergei M. / Halton, John H. / Heinrich, Stefan / Kalos, Malvin H. / Lepingle, D. / Makarov, Roman / Mascagni, Michael / Mathe, Peter / Niederreiter, Harald / Platen, Eckhard / Sawford, Brian R. / Schmid, Wolfgang Ch. / Schoenmakers, John / Simonov, Nikolai A. / Sobol, Ilya M. / Spanier, Jerry / Talay, Denis
4 Issues per year
CiteScore 2017: 0.67
SCImago Journal Rank (SJR) 2017: 0.417
Source Normalized Impact per Paper (SNIP) 2017: 0.860
Mathematical Citation Quotient (MCQ) 2016: 0.33
- Online
- ISSN
- 1569-3961
Some results of error evaluation for a non-Gaussian simulation method
Jean-Luc Akian / Bénédicte Puig
Published Online: | DOI: https://doi.org/10.1515/156939604323091207
30,00 € / $42.00 / £23.00
Get Access to Full TextKey Words: Monte-Carlo simulation,; non-Gaussian process,; Hermite polynomials,; maximum entropy principle.
About the article
Published Online:
Published in Print: 2004-03-01
Citation Information: Monte Carlo Methods and Applications mcma, Volume 10, Issue 1, Pages 51–68, ISSN (Online) 1569-3961, ISSN (Print) 0929-9629, DOI: https://doi.org/10.1515/156939604323091207.
Comments (0)