Some results of error evaluation for a non-Gaussian simulation method

Some results of error evaluation for a non-Gaussian simulation method

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 object...

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Veröffentlicht in:Monte Carlo methods and applications 2004-03, Vol.10 (1), p.51-68
Hauptverfasser: Akian, Jean-Luc, Puig, Bénédicte
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Sprache:eng
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Hermite polynomials
maximum entropy principle
Monte-Carlo simulation
non-Gaussian process
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description 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.
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subjects Hermite polynomials
maximum entropy principle
Monte-Carlo simulation
non-Gaussian process
title Some results of error evaluation for a non-Gaussian simulation method
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