Confidence intervals for quantiles with standardized time series

Schruben (1983) developed standardized time series (STS) methods to construct confidence intervals (CIs) for the steady-state mean of a stationary process. STS techniques cancel out the variance constant in the asymptotic distribution of the centered and scaled estimator, thereby eliminating the nee...

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Bibliographic Details
Published in2013 Winter Simulations Conference (WSC) pp. 601 - 612
Main Authors Calvin, James M., Nakayama, Marvin K.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2013
Subjects
Computational modeling
Kernel
Monte Carlo methods
Random variables
Steady-state
Time series analysis
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Summary:Schruben (1983) developed standardized time series (STS) methods to construct confidence intervals (CIs) for the steady-state mean of a stationary process. STS techniques cancel out the variance constant in the asymptotic distribution of the centered and scaled estimator, thereby eliminating the need to consistently estimate the asymptotic variance to obtain a CI. This is desirable since estimating the asymptotic variance in steady-state simulations presents nontrivial challenges. Difficulties also arise in estimating the asymptotic variance of a quantile estimator. We show that STS methods can be used to build CIs for a quantile for the case of crude Monte Carlo (i.e., no variance reduction) with independent and identically distributed outputs. We present numerical results comparing CIs for quantiles using STS to other procedures.
ISSN:0891-7736
1558-4305
DOI:10.1109/WSC.2013.6721454