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):
Report Date:
1987-04-01
Pagination or Media Count:
42.0
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 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.
Descriptors:
Subject Categories:
- Statistics and Probability