@article{hensim04,
	abstract = {We introduce adaptive-simulation schemes for estimating performance measures for stochastic systems based on the method of control variates. We consider several possible methods for adaptively tuning the control-variate estimators, and describe their asymptotic properties. Under certain assumptions, including the existence of a ``perfect control variate'', all of the estimators considered converge faster than the canonical rate of $n^{−1/2}$, where $n$ is the simulation runlength. Perfect control variates for a variety of stochastic processes can be constructed from ``approximating martingales.'' We prove a central limit theorem for an adaptive estimator that converges at rate $n^{−1}\sqrt{\ln n}$. A similar estimator converges at rate $n^{−1}$. An exponential rate of convergence is also possible under suitable conditions.},
	author = {Shane G. Henderson and Burt Simon},
	date-added = {2016-01-10 16:07:54 +0000},
	date-modified = {2016-01-10 16:07:54 +0000},
	journal = {Journal of Applied Probability},
	pages = {859--876},
	title = {Adaptive simulation using perfect control variates},
	url_paper = {pubs/HendersonSimon.pdf},
	volume = {41},
	year = {2004}}

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Perfect control variates for a variety of stochastic processes can be constructed from ``approximating martingales.'' We prove a central limit theorem for an adaptive estimator that converges at rate $n^{−1}\\sqrt{łn n}$. A similar estimator converges at rate $n^{−1}$. An exponential rate of convergence is also possible under suitable conditions.","author":[{"firstnames":["Shane","G."],"propositions":[],"lastnames":["Henderson"],"suffixes":[]},{"firstnames":["Burt"],"propositions":[],"lastnames":["Simon"],"suffixes":[]}],"date-added":"2016-01-10 16:07:54 +0000","date-modified":"2016-01-10 16:07:54 +0000","journal":"Journal of Applied Probability","pages":"859–876","title":"Adaptive simulation using perfect control variates","url_paper":"pubs/HendersonSimon.pdf","volume":"41","year":"2004","bibtex":"@article{hensim04,\n\tabstract = {We introduce adaptive-simulation schemes for estimating performance measures for stochastic systems based on the method of control variates. We consider several possible methods for adaptively tuning the control-variate estimators, and describe their asymptotic properties. Under certain assumptions, including the existence of a ``perfect control variate'', all of the estimators considered converge faster than the canonical rate of $n^{−1/2}$, where $n$ is the simulation runlength. Perfect control variates for a variety of stochastic processes can be constructed from ``approximating martingales.'' We prove a central limit theorem for an adaptive estimator that converges at rate $n^{−1}\\sqrt{\\ln n}$. A similar estimator converges at rate $n^{−1}$. An exponential rate of convergence is also possible under suitable conditions.},\n\tauthor = {Shane G. 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