An Investigation of Finite Sample Behavior of Confidence Interval Estimation Procedures in Computer Simulation
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Authors
Sargent, Robert G.
Goldsman, David
Kang, Keebom
Subjects
Simulations
Statistical analysis
Confidence interval
Time series
Estimation
Statistical analysis
Confidence interval
Time series
Estimation
Advisors
Date of Issue
1991-04
Date
1991-04
Publisher
Monterey, California. Naval Postgraduate School
Language
eng
Abstract
Investigated are the small sample behavior and convergence properties of confidence interval estimators (CIE's) for the mean of a stationary discrete process. We consider CIE's arising from nonoverlapping batch means, overlapping batch means, and standardized time series, all of which are commonly used in discrete-event simulation. For a specific CIE, the performance measures of interest include the coverage probability, and the expected value and variance of the half-length. We use both empirical and analytical methods to make detailed comparisons regarding the behavior of the CIE's for a variety of stochastic processes. All of the CIE's under study are asymptotically valid; however, they are usually invalid for small sample sizes. We find that for small samples, the bias of the variance parameter estimator figures significantly in CIE coverage performance-the less bias the better. A Secondary role is played by the Marginal distribution of the stationary process. Not all CIE's are equal - some require fewer observations before manifesting the properties for CIE validity
Type
Technical Report
Description
Series/Report No
Department
Administrative Sciences
Organization
Graduate School of Operational and Information Sciences (GSOIS)
Identifiers
NPS Report Number
NPS-AS-91-009
Sponsors
Naval Postgraduate School, Monterey, California.
Funder
O&MN Direct Funding
Format
NA
Citation
Distribution Statement
Approved for public release; distribution is unlimited.