Regenerative Simulation Using Internal Controls.

Abstract

A new procedure is developed to exploit the asymptotic properties of appropriately defined controls in order to improve the efficiency of system performance estimators obtained from a simulation experiment . Two types of standardized variates are formulated and their joint convergence in distribution to multivariate normality is established. This result is the basis for applying multinormal regression theory to the construction of valid confidence intervals for steady-state parameters. A two-stage procedure is developed. In the first stage, standardized flow variates are applied to the denominator of the classical regenerative ratio estimator to reduce its inherent bias. In the second stage, standardized service-time variates are applied to the numerator of the new ratio to reduce the variance of the resulting double multivariate ratio estimator. A new multivariate test for normality is also developed based upon the univariate Shapiro-Wilk test. The complete variance reduction procedure is validated on an analytically tractable closed Jackson queueing system. On the basis of experimental results together with the established theoretical characteristics of the technique, guidelines are presented for the practical application of the method.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1982
Accession Number
ADA119740

Entities

People

  • Jan Denise Eakle-cardinal

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Energy and Power Technologies
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Air Force
  • Computational Fluid Dynamics
  • Computational Science
  • Data Science
  • Equations
  • Information Processing
  • Information Science
  • Knowledge Management
  • Mathematical Models
  • New York
  • Normal Distribution
  • Operations Research
  • Probability
  • Random Variables
  • Statistical Algorithms
  • Stochastic Processes
  • Surveys

Fields of Study

  • Mathematics

Readers

  • Mathematical Modeling and Probability Theory.
  • Regression Analysis.
  • Statistical inference.