Derandomizing variance estimators. Henderson, S. & Glynn, P. Operations Research, 47:907–916, 1999. ![Derandomizing variance estimators [pdf]](https://bibbase.org/img/filetypes/pdf.svg "pdf")
Paper abstract bibtex One may consider a discrete-event simulation as a Markov chain evolving on a suitably rich state space. One way that regenerative cycles may be constructed for general state-space Markov chains is to generate auxiliary coin- flip random variables at each transition, with a regeneration occurring if the coin-flip results in a success. The regenerative cycles are therefore randomized with respect to the sequence of states visited by the Markov chain. The point estimator for a steady-state performance measure does not depend on the cycle structure of the chain, but the variance estimator (that defines the width of a confidence interval for the performance measure) does. This implies that the variance estimator is randomized with respect to the visited states. We show how to ``derandomize'' the variance estimator through the use of conditioning. A new variance estimator is obtained that is consistent, and has lower variance than the standard estimator.
@article{hengly99derandom,
abstract = {One may consider a discrete-event simulation as a Markov chain evolving on a suitably rich state space. One way that regenerative cycles may be constructed for general state-space Markov chains is to generate auxiliary coin- flip random variables at each transition, with a regeneration occurring if the coin-flip results in a success. The regenerative cycles are therefore randomized with respect to the sequence of states visited by the Markov chain. The point estimator for a steady-state performance measure does not depend on the cycle structure of the chain, but the variance estimator (that defines the width of a confidence interval for the performance measure) does. This implies that the variance estimator is randomized with respect to the visited states. We show how to ``derandomize'' the variance estimator through the use of conditioning. A new variance estimator is obtained that is consistent, and has lower variance than the standard estimator.},
author = {Henderson, S.~G. and Glynn, P.~W.},
date-added = {2016-01-10 16:07:54 +0000},
date-modified = {2016-01-10 16:07:54 +0000},
journal = {Operations Research},
pages = {907--916},
title = {Derandomizing variance estimators},
url_paper = {pubs/Derand.pdf},
volume = 47,
year = 1999}
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