The use of variance reduction, relative error and bias in testing the performance of M/G/1 retrial queues estimators in Monte Carlo simulation

Tamiti Kenza (), Ourbih-Tari Megdouda (), Aloui Abdelouhab () and Idjis Khelidja ()
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Tamiti Kenza: Laboratoire de Mathématiques Appliquées, FSE, Université de Bejaia, 06000Bejaia, Algeria
Ourbih-Tari Megdouda: Centre Universitaire Morsli Abdellah de Tipaza, 42000Tipaza; and Laboratoire de Mathématiques Appliquées, FSE, Université de Bejaia, 06000 Bejaia, Algeria
Aloui Abdelouhab: LiMed, FSE, Université de Bejaia, 06000Bejaia, Algeria
Idjis Khelidja: Laboratoire de Mathématiques Appliquées, FSE, Université de Bejaia, 06000Bejaia, Algeria

Monte Carlo Methods and Applications, 2018, vol. 24, issue 3, 165-178

Abstract: This paper deals with Monte Carlo simulation and focuses on the use of the concepts of variance reduction, relative error and bias in testing the performance of stationary M/G/1 retrial queues estimators using either Random Sampling (RS) or Refined Descriptive Sampling (RDS) to generate input samples. For this purpose, a software under Linux system using the C compiler was designed and realized providing the performance measures of such system and the statistical concepts of bias, relative error and accuracy using both sampling methods. As a conclusion, it has been shown that the performance of stationary M/G/1 retrial queues estimators is better using RDS than RS and sometimes by a substantial variance reduction factor.

Keywords: Retrial queues; sampling methods; Monte Carlo simulation; estimation; variance reduction (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1515/mcma-2018-0015

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