Confidence intervals for quantiles with standardized time series

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publicationProceedings of the 2013 Winter Simulation Conference - Simulation
Subtitle of host publicationMaking Decisions in a Complex World, WSC 2013
Pages601-612
Number of pages12
DOIs
StatePublished - 2013
Event2013 43rd Winter Simulation Conference - Simulation: Making Decisions in a Complex World, WSC 2013 - Washington, DC, United States
Duration: Dec 8 2013Dec 11 2013

Publication series

NameProceedings of the 2013 Winter Simulation Conference - Simulation: Making Decisions in a Complex World, WSC 2013

Other

Other2013 43rd Winter Simulation Conference - Simulation: Making Decisions in a Complex World, WSC 2013
Country/TerritoryUnited States
CityWashington, DC
Period12/8/1312/11/13

All Science Journal Classification (ASJC) codes

  • Modeling and Simulation

Fingerprint

Dive into the research topics of 'Confidence intervals for quantiles with standardized time series'. Together they form a unique fingerprint.

Cite this