@inproceedings{88ae3d3845ee4017b4fd14968a8ce163,
title = "Threshold bootstrap: a new approach to simulation output analysis",
abstract = "The threshold bootstrap (TB) is a promising new method of inference for a single autocorrelated data series, such as the output of a discrete event simulation. The method works by resampling runs of data created when the series crosses a threshold level, such as the series mean. We performed a Monte Carlo evaluation of the TB using three types of data: white noise, first-order autoregressive, and delays in an M/M/1 queue. The results show that the TB produces accurate and tight estimates of the standard deviation of the sample mean and valid confidence intervals.",
author = "Kim, {Y. B.} and Willemain, {T. R.} and J. Haddock and Runger, {G. C.}",
year = "1993",
month = dec,
day = "1",
language = "English (US)",
isbn = "0780313801",
series = "Winter Simulation Conference Proceedings",
publisher = "Publ by IEEE",
pages = "498--502",
editor = "Evans, {Gerald W.} and Mansooren Mollaghasemi and Russell, {Edward C.} and Biles, {William E.}",
booktitle = "Winter Simulation Conference Proceedings",
}