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Note---New Confidence Interval Estimators Using Standardized Time Series

Goldsman, David; Schruben, Lee - In: Management Science 36 (1990) 3, pp. 393-397

We develop new asymptotically valid confidence interval estimators (CIE's) for the underlying mean of a stationary simulation process. The new estimators are weighted generalizations of Schruben's standardized time series area CIE. We show that the weighted CIE's have the same asymptotic...

Persistent link: https://www.econbiz.de/10009214300

Properties of Standardized Time Series Weighted Area Variance Estimators

Goldsman, David; Meketon, Marc; Schruben, Lee - In: Management Science 36 (1990) 5, pp. 602-612

We wish to estimate the variance of the sample mean from a continuous-time stationary stochastic process. This article expands on the results of a technical note (Goldsman and Schruben 1990) by using the theory of standardized time series to investigate weighted generalizations of Schruben's...

Persistent link: https://www.econbiz.de/10009218065

Notes: Conditions for the Applicability of the Regenerative Method

Glynn, Peter W.; Iglehart, Donald L. - In: Management Science 39 (1993) 9, pp. 1108-1111

The regenerative method for estimating steady-state parameters is one of the basic methods in simulation output analysis. This method depends on central limit theorems for regenerative processes and weakly consistent estimates for the variance constants arising in the central limit theorems. A...

Persistent link: https://www.econbiz.de/10009214468

Standardized Time Series Lp-Norm Variance Estimators for Simulations

Tokol, Gamze; Goldsman, David; Ockerman, Daniel H.; … - In: Management Science 44 (1998) 2, pp. 234-245

This paper studies a class of estimators for the variance parameter of a stationary stochastic process. The estimators are based on L<sub>p</sub> norms of standardized time series, and they generalize previously studied estimators due to Schruben. We show that the new estimators have some desirable...

Persistent link: https://www.econbiz.de/10009214570

BAYESIAN FORECASTING USING STOCHASTIC SIMULATION

Muñoz Negrón, David F. - In: Journal of Economics, Finance and Administrative Science 14 (2009) 26, pp. 7-26

on the point forecast is assessed through the estimation of a conditional variance and a prediction interval. We discuss … how to construct asymptotic conﬁdence intervals to assess the estimation error for the estimators obtained using …

Persistent link: https://www.econbiz.de/10009319482

Folded overlapping variance estimators for simulation

Meterelliyoz, Melike; Alexopoulos, Christos; Goldsman, David - In: European Journal of Operational Research 220 (2012) 1, pp. 135-146

We propose and analyze a new class of estimators for the variance parameter of a steady-state simulation output process. The new estimators are computed by averaging individual estimators from “folded” standardized time series based on overlapping batches composed of consecutive...

Persistent link: https://www.econbiz.de/10011052497

A Batch Means Methodology for Estimation of a Nonlinear Function of a Steady-State Mean

Muñoz, David F.; Glynn, Peter W. - In: Management Science 43 (1997) 8, pp. 1121-1135

We study the estimation of steady-state performance measures from an \frak{R}<sup>d</sup>-valued stochastic process Y … = {Y(t): t \ge 0} representing the output of a simulation. In many applications, we may be interested in the estimation of … more general problem---the estimation of a (nonlinear) function f(r) of r. We propose a batch-means-based methodology that …

Persistent link: https://www.econbiz.de/10009198064

A note on bias and mean squared error in steady-state quantile estimation

Muñoz, David F.; Ramírez-López, Adán - In: Operations research letters 43 (2015) 4, pp. 374-377

Persistent link: https://www.econbiz.de/10011372470

Efficient VaR and CSaR measurement via stochastic kriging

Chen, Xi; Kim, Kyoung-Kuk - In: INFORMS journal on computing : JOC 28 (2016) 4, pp. 629-644

Persistent link: https://www.econbiz.de/10011617535

A Bayesian framework for quantifying uncertainty in stochastic simulation

Xie, Wei; Nelson, Barry L.; Barton, Russell R. - In: Operations research 62 (2014) 6, pp. 1439-1452

Persistent link: https://www.econbiz.de/10010471836

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