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We propose an algorithm to generate refined descriptive samples from dependent random variables for estimation of expectations of functions of output variables using the Iman and Conover algorithm to transform the dependent variables to independent ones. Hence, the asymptotic variance of such an estimate in case of dependent input random variables is proved, using a result from to be less than that obtained using simple random sampling.

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Adaptive Refined Descriptive Sampling Algorithm for Dependent Variables Using Iman and Conover Method in Monte Carlo Simulation

  • 会议论文

  • 发表日期：

    2025年

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  • Siham Kebaili,
  • Ourbih Megdouda

摘要

We propose an algorithm to generate refined descriptive samples from dependent random variables for estimation of expectations of functions of output variables using the Iman and Conover algorithm to transform the dependent variables to independent ones. Hence, the asymptotic variance of such an estimate in case of dependent input random variables is proved, using a result from to be less than that obtained using simple random sampling.