Simulation Factor Screening Using Harmonic Analysis

Morrice, Douglas J.; Schruben, Lee W.

doi:10.1287/mnsc.39.12.1459

Cited by 26 publications

(29 citation statements)

References 13 publications

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“…This is because the run length, as opposed to the number of runs, is increased to accommodate more input parameters. 71 The method computes a signal-tonoise ratio (SNR) for each parameter, derived from the results of the two simulation runs. This statistic is then compared against an F-statistic; the result of this comparison determines whether or not the parameter is significant in the model.…”

Section: Frequency Domain Methodologymentioning

confidence: 99%

“…These driving frequencies are part of a set called the Fourier frequencies, which are given by A second simulation run (the noise run) is required to obtain an estimate of the variance of the error process, with the input parameters being held fixed so that the variation in the output is due solely to noise in the system. 71 For each run, and for each parameter, a statistic called the periodogram ordinate, I( j ), is calculated:…”

Section: Frequency Domain Methodologymentioning

confidence: 99%

See 1 more Smart Citation

Methods for expected value of information analysis in complex health economic models: developments on the health economics of interferon-β and glatiramer acetate for multiple sclerosis

Tappenden

Chilcott²,

Eggington³

et al. 2004

Health Technol Assess

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Section: Frequency Domain Methodologymentioning

confidence: 99%

Section: Frequency Domain Methodologymentioning

confidence: 99%

Methods for expected value of information analysis in complex health economic models: developments on the health economics of interferon-β and glatiramer acetate for multiple sclerosis

Tappenden

Chilcott²,

Eggington³

et al. 2004

Health Technol Assess

View full text Add to dashboard Cite

show abstract

“…Morrice and Schruben (1993) proposed a variation of FDE called harmonic analysis. Here the simulation output is regressed onto explanatory variables of the form sinð2po i ðtÞÞ and cosð2po i ðtÞÞ for all indicator frequencies o i .…”

Section: Some Details Caveats and Refinementsmentioning

confidence: 99%

So many factors, so little time…Simulation experiments in the frequency domain

Sanchez

Moeeni

Sánchez

2006

International Journal of Production Economics

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“…Frequency domain experiments vary the parameters within a single run according to given functions so that metamodels can be constructed from very few runs. For details, see Morrice (1993) and Sanchez (1987). Here, we look at parameter variation within a run to better account for input uncertainty in the real world: not only are we unsure of the real parameter value, but we may not be sure that the parameter remains constant.…”

Section: Introductionmentioning

confidence: 99%

Interval-Based Simulation to Model Input Uncertainty in Stochastic Lanchester Models

Singham

Batarseh

2013

Military Oper Res

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I nterval-based simulation (IBS) has been proposed to model input uncertainty in discrete-event simulation. The foundation of this new simulation paradigm is imprecise probability, which models systems under both aleatory and epistemic uncertainties. The statistical distribution parameters in IBS are represented by intervals instead of precise real numbers. This paper discusses how the IBS approach can be applied to stochastic Lanchester models that are used in combat simulation to better account for input parameter uncertainty. The advantages of this approach are explored in comparison with secondorder Monte Carlo simulation. Using IBS, an improved estimate of the probability of a team winning a battle is calculated by exploiting the interval structure. By resampling from intervals to determine event times, we can separate the effect of parameter uncertainty from random number generator uncertainty to estimate the probability that one team will win for a given stream of random numbers used in a single replication. Additionally, we show how our method can be used to improve the reliability of stochastic Lanchester results by accounting for different skill levels within each team, and show how the interval structure can be used to highlight the disproportionate effect of the first few encounters in the battle.

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Simulation Factor Screening Using Harmonic Analysis

Cited by 26 publications

References 13 publications

Methods for expected value of information analysis in complex health economic models: developments on the health economics of interferon-β and glatiramer acetate for multiple sclerosis

Methods for expected value of information analysis in complex health economic models: developments on the health economics of interferon-β and glatiramer acetate for multiple sclerosis

So many factors, so little time…Simulation experiments in the frequency domain

Interval-Based Simulation to Model Input Uncertainty in Stochastic Lanchester Models

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