A Low Bias Steady-State Estimator for Equilibrium Processes.
STANFORD UNIV CA DEPT OF OPERATIONS RESEARCH
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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 XXt t or 0 is a real valued non arithmetic equilibrium process, then an asymptotic relation of the form integral from 0 to t of EXsds alpha t beta o1 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-bart. The reduced bias is obtained without adversely affecting the asymptotic convergence rate. Keywords Bias Harris recurrent Marko chains Regenerative process Simulation Steady state.
- Statistics and Probability