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Monte Carlo is one of the most prominent methods used to obtain integrals. This paper presents the techniques of simple random sampling (SRSampling) and ordered set sampling (RSSampling) as two methods for estimating Monte Carlo integrals. We also used the improved methods of both SRS and RSS to increase the accuracy of the required solutions. In fact, in the improved SRS algorithm, we first increase the homogeneity of the random samples taken on the interval [0,1] to achieve additional uniformity. In addition, we present the RSSampling package, which facilitates the sampling operations based on the classical principles of RSS, where both RSS sampling and the Monte Carlo method enhance the efficiency and accuracy of statistical estimates. Finally, we provide an example to illustrate the benefits of the RSSampling package.

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Simulation for Data Analysis Based on Simple Random Sampling and Ranked Set Sampling in Monte Carlo Estimation

  • 会议论文

  • 发表日期：

    2025年

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  • Ahmad Nouraldin,
  • Behrouz Fathi Vajargah

摘要

Monte Carlo is one of the most prominent methods used to obtain integrals. This paper presents the techniques of simple random sampling (SRSampling) and ordered set sampling (RSSampling) as two methods for estimating Monte Carlo integrals. We also used the improved methods of both SRS and RSS to increase the accuracy of the required solutions. In fact, in the improved SRS algorithm, we first increase the homogeneity of the random samples taken on the interval [0,1] to achieve additional uniformity. In addition, we present the RSSampling package, which facilitates the sampling operations based on the classical principles of RSS, where both RSS sampling and the Monte Carlo method enhance the efficiency and accuracy of statistical estimates. Finally, we provide an example to illustrate the benefits of the RSSampling package.