Antithetic Power Transformation in Monte Carlo Simulation: Correcting Hidden Errors in the Response Variable

Dennis Ridley () and Pierre Ngnepieba
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Dennis Ridley: School of Business & Industry, Florida A&M University, Tallahassee, FL 32307, USA
Pierre Ngnepieba: Department of Mathematics, Florida A&M University, Tallahassee, FL 32307, USA

Mathematics, 2023, vol. 11, issue 9, 1-12

Abstract: Monte Carlo simulation is performed with uniformly distributed U(0,1) pseudo-random numbers. Because the numbers are generated from a mathematical formula, they will contain some serial correlation, even if very small. This serial correlation becomes embedded in the correlation structure of the response variable. The response variable becomes an asynchronous time series. This leads to hidden errors in the response variable. The purpose of this paper is to illustrate how this happens and how it can be corrected. The method is demonstrated for the case of a simple queue for which the time in the system is known exactly from theory. The paper derives the correlation between an exponential random variable and its antithetic counterpart obtained by power transform with an infinitesimal negative exponent.

Keywords: inverse correlation; variance reduction; antithetic random variates; simulation model bias; bias reduction (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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