Simulation output analysis using integrated paths II: Low bias estimators

ABSTRACT

This article is a sequel to a previous article that introduced a class of variance estimators for steady-state simulation output analysis. The estimators were constructed by applying a quadratic function to a vector obtained from iterated integrations of the simulation output. The previous article concentrated on deriving the limiting distributions of the estimators and on their computational efficiency for a particular choice of quadratic function. The present article considers estimators constructed from different quadratic functions, chosen mainly to reduce bias compared to the estimators of the previous article. Overlapping and nonoverlapping batch means versions of the estimators are discussed.

REFERENCES

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INDEX TERMS

Primary Classification:
  C. Computer Systems Organization
  C.4 PERFORMANCE OF SYSTEMS
      Subjects: Performance attributes

Additional Classification:
  G. Mathematics of Computing
  G.3 PROBABILITY AND STATISTICS
      Subjects: Statistical computing; Probabilistic algorithms (including Monte Carlo); Stochastic processes

  I. Computing Methodologies
  I.6 SIMULATION AND MODELING
      I.6.6 Simulation Output Analysis

General Terms:
Algorithms, Performance, Theory

Keywords:
Variance reduction, efficiency improvement