A New Class of Strongly Consistent Variance Estimators for Steady-State Simulations.

Abstract

The principal problem associated with steady-state simulation is the estimation of the variance term in an associated central limit theorem. This paper develops several strongly consistent estimates for this term using the strong approximations available for Brownian motion. A comparison of rates of convergence is given for a variety of estimators. Keywords: confidence intervals; regenerative simulation; simulation output analysis; strongly consistent estimation.

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Document Details

Document Type
Technical Report
Publication Date
Oct 01, 1986
Accession Number
ADA178861

Entities

People

  • Donald Iglehart
  • Peter W. Glynn

Organizations

  • Stanford University

Tags

DTIC Thesaurus Topics

  • Brownian Motion
  • Convergence
  • Estimators
  • Intervals
  • Markov Chains
  • Military Research
  • New York
  • Operations Research
  • Probability
  • Random Variables
  • Sequences
  • Simulations
  • Steady State
  • Stochastic Processes
  • United States
  • Universities

Fields of Study

  • Mathematics

Readers

  • Statistical inference.