Multivariate inference in stationary simulation using batch means

Chen, Der-Fa R.; Seila, Andrew F.

doi:10.1145/318371.318436

Cited by 31 publications

(12 citation statements)

References 2 publications

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“…of the above inequality is equal to 0. If the overall confidence level must be at least 1−α, then the α i can be chosen so that m i=1 α i = α. Multivariate estimation methods are described in Charnes (1989Charnes ( , 1990Charnes ( , 1991 and Chen and Seila (1987).…”

Section: Multivariate Estimationmentioning

confidence: 99%

A Comprehensive Review of Methods for Simulation Output Analysis

Alexopoulos

2006

Proceedings of the 2006 Winter Simulation Conference

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Section: Multivariate Estimationmentioning

confidence: 99%

A Comprehensive Review of Methods for Simulation Output Analysis

Alexopoulos

2006

Proceedings of the 2006 Winter Simulation Conference

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“…Chen and Seila (1987) discuss the application of the multivariate-batch-means method to steady-state, synchronous simulation output. Consider a stationary process that produces a sequence of d-dimensional vector-valued obser- …”

Section: The Mbm Methodsmentioning

confidence: 99%

“…There has been some interest recently among researchers in the problem of making statistical inferences simultaneously on more than one output measure of interest in simulation modeling (Chen and Seila 1987;Yang and Nelson 1988;Charnes and Kelton 1988). One technique that has received attention is the multivariate batch-means (MBM) method, perhaps because it is a generalization of the widely-used univariate batch-means method and thus has the potential to be the most widely-used multivariate technique.…”

Section: Introductionmentioning

confidence: 99%

Power comparisons for the multivariate batch-means method

Charnes¹

1990 Winter Simulation Conference Proceedings

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The multivariate batch-means (MBM) method of analyzing simulation output has the potential to be a widely-used tool by practitioners. This paper presents a power comparison of selected statistics that can be used for choosing the batches, which is one of the key elements in the successful use of the MBM method. Explicit procedures are given for five batch-selection criteria, and some guidelines for the proper use of the MBM method are stated.

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“…If the overall confidence level must be at least lOO(1 -C Y ) percent, then the ai's can be chosen so that CY^ = a. The existing multivariate estimation methods include Charnes (1989Charnes ( , 1990Charnes ( , 1991 and Chen and Seila (1987).…”

Section: Multivariate Estimationmentioning

confidence: 99%

Advanced methods for simulation output analysis

Alexopoulos¹

Winter Simulation Conference Proceedings, 1995.

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This paper reviews statistical methods for analyzing output data from computer simulations of single systems. In particular, it focuses on the problems of choosing initial conditions and estimating steadystate system parameters. The estimation techniques include the replication/deletion approach, the regenerative method, the batch means method, the standardized time series method, the autoregressive method, and the spectral estimation method.

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Multivariate inference in stationary simulation using batch means

Cited by 31 publications

References 2 publications

A Comprehensive Review of Methods for Simulation Output Analysis

A Comprehensive Review of Methods for Simulation Output Analysis

Power comparisons for the multivariate batch-means method

Advanced methods for simulation output analysis

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