Permuted standardized time series for steady-state simulations

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10 Scopus citations

Abstract

We describe an extension procedure for constructing new standardized time series procedures from existing ones. The approach is based on averaging over sample paths obtained by permuting path segments. Analytical and empirical results indicate that permuting improves standardized time series methods. We compare permuting to an alternative extension procedure known as batching. We demonstrate the permuting method by applying it to estimators based on the maximum and the area of a normalized path.

Original languageEnglish (US)
Pages (from-to)351-368
Number of pages18
JournalMathematics of Operations Research
Volume31
Issue number2
DOIs
StatePublished - 2006

All Science Journal Classification (ASJC) codes

  • Mathematics(all)
  • Computer Science Applications
  • Management Science and Operations Research

Keywords

  • Confidence intervals
  • Functional central limit theorem
  • Steady-state simulation

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