
Accession Number : ADA180554
Title : A Low Bias SteadyState 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 Xbar(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