A Low Bias Steady-State Estimator for Equilibrium Processes.

Abstract

This paper concerns the steady state structure of equilibrium processes; an equilibrium process is a generalization of regenerative process which is useful for studying Harris recurrent Markov chains. Specifically, if X=(X(t) : t > or = 0) is a real valued non arithmetic equilibrium process, then an asymptotic relation of the form integral from 0 to t of EX(s)ds)= alpha t + beta + o(1) as t approaches infinity is obtained. This asymptotic expression is then used to obtain a Monte Carlo estimator for the steady state mean alpha which has lower bias than the traditional sample mean estimator X-bar(t). The reduced bias is obtained without adversely affecting the asymptotic convergence rate. Keywords: Bias; Harris recurrent Marko chains; Regenerative process; Simulation; Steady state.

Document Details

Document Type
Technical Report
Publication Date
Apr 01, 1987
Accession Number
ADA180554

Entities

People

  • Peter W. Glynn

Organizations

  • Stanford University

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Arithmetic
  • Convergence
  • Estimators
  • Integrals
  • Markov Chains
  • Mathematics
  • Simulations
  • Steady State

Fields of Study

  • Mathematics

Readers

  • Approximation Theory.
  • Mathematical Modeling and Probability Theory.