Derandomizing variance estimators
成果类型：
Article
署名作者：
Henderson, SG; Glynn, PW
署名单位：
University of Auckland; Stanford University
刊物名称：
OPERATIONS RESEARCH
ISSN/ISSBN：
0030-364X
DOI：
10.1287/opre.47.6.907
发表日期：
1999
页码：
907-916
关键词：
摘要：
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.