Optimal subset selection of stochastic model using statistical hypothesis test

Seon Han Choi, Tag Gon Kim

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

This paper proposes an improved algorithm for the optimal subset selection of a stochastic simulation model. The algorithm uses a statistical hypothesis test based on frequentist inference to evaluate the uncertainty about the selection, and it distributes simulation resources to designs for minimizing the uncertainty in each iteration. Several experiments demonstrate the improved performance compared to the other algorithms, and the performance increases significantly as the noise of the model increases. As a result, its high robustness to noise allows the algorithm to efficiently analyze real-world problems.

Original languageEnglish
Pages (from-to)557-564
Number of pages8
JournalIEEE Transactions on Systems, Man, and Cybernetics: Systems
Volume48
Issue number4
DOIs
StatePublished - Apr 2018

Keywords

  • Optimal subset selection
  • ranking and selection (R&S)
  • simulation-based optimization (SBO)
  • statistical hypothesis test
  • stochastic model

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