Estimating steady-state distributions via simulation-generated histograms

Chen, E. Jack; Kelton, W. David

doi:10.1016/j.cor.2006.05.015

Cited by 35 publications

(30 citation statements)

References 7 publications

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“…Simulation experiments are usually costly and time consuming to run, and therefore this estimator appears especially well suited to that context. The use of exponential epi-spline estimators of output densities differs substantially from current analysis of simulation output; see Alexopolous (2006) for a general overview of simulation output analysis and Chen and Kelton (2008) for density estimation methods using histograms.…”

Section: Introductionmentioning

confidence: 99%

Density estimation of simulation output using exponential epi-splines

Singham

Røyset

Wets

2013

2013 Winter Simulations Conference (WSC)

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The density of stochastic simulation output provides more information on system performance than the mean alone. However, density estimation methods may require large sample sizes to achieve a certain accuracy or desired structural properties. A nonparametric estimation method based on exponential epi-splines has shown promise to overcome this difficulty by incorporating qualitative and quantitative information that reduces the space of possible density estimates substantially. Such 'soft' information may come in the form of the knowledge of a non-negative support, unimodality, and monotonicity, and is often available in simulation applications. We examine this method for output analysis of stochastic systems with fixed input parameters, and for a model with stochastic input parameters, with an emphasis on the use of derivative information.

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Section: Introductionmentioning

confidence: 99%

Density estimation of simulation output using exponential epi-splines

Singham

Røyset

Wets

2013

2013 Winter Simulations Conference (WSC)

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“…Chen and Kelton (2008) control the precision of quantile estimates by ensuring that the p quantile estimatorx p satisfies the following:…”

Section: Methods Of Computationmentioning

confidence: 99%

“…To avoid storing and sorting the entire sequence, we use a modified version of the histogram-approximation procedure of Chen and Kelton (2008) to estimate quantiles. Instead of determining the sample size and grid sizes dynamically, we use pre-determined sample size and grid sizes.…”

Section: Methods Of Computationmentioning

confidence: 99%

An enhanced lognormal selection procedure

Chen

2010

Discrete Event Dyn Syst

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John and Chen (IEEE Trans Reliab 55(1): [135][136][137][138][139][140][141][142][143][144][145][146][147][148] 2006) propose an exact two-stage solution based on the Least Favorable Configuration (LFC) to the ranking and selection problem of determining the best system from k lognormal populations. Lognormal density is commonly used to model certain lifetimes in reliability and survival analysis. It is known that selection procedures that are developed based on the LFC are conservative and become inefficient when the number of systems is large. We propose to take into account the differences of sample values within the parameter of interest when determining sample sizes, which can significantly increase the efficiency of the selection procedures. We also sequentialize the selection procedure and provide a procedure for estimating the constant needed to apply the solution. An experimental performance evaluation demonstrates the validity and efficiency of the enhanced lognormal selection procedure.

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“…Chen and Kelton (2006) propose two heuristic algorithms to determine the simulation run length for estimating quantiles that satisfy a pre-specified precision requirement. Chen and Kelton (2008) propose obtaining quantile estimates at certain grid points and then using the Lagrange interpolation to estimate any qth quantile. The grid point approach reduces the storage requirement but introduces bias.…”

Section: Quantile Estimationmentioning

confidence: 99%