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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.
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An investigation of finite sample behavior of confidence interval estimation procedures in computer simulation
1991, Naval Postgraduate School, Available from National Technical Information Service
in English
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Edition Notes
Cover title.
"NPS-AS-91-009."
"April 1991."
AD A238 744.
Includes bibliographical references (p. 17-19).
aq/ /aq cc:9116 10/28/97
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