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