Accession Number : ADA180554

Title :   A Low Bias Steady-State Estimator for Equilibrium Processes.

Descriptive Note : Technical rept.,

Corporate Author : STANFORD UNIV CA DEPT OF OPERATIONS RESEARCH

Personal Author(s) : Glynn,Peter W.

Report Date : APR 1987

Pagination or Media Count : 42

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.

Descriptors :   *EQUILIBRIUM(GENERAL), *MARKOV PROCESSES, ASYMPTOTIC SERIES, BIAS, ESTIMATES, MONTE CARLO METHOD, REDUCTION, SIMULATION, MEAN, STEADY STATE, REGENERATION(ENGINEERING), CONVERGENCE

Subject Categories : Statistics and Probability

Distribution Statement : APPROVED FOR PUBLIC RELEASE