A Procedure for Generating Batch-Means Confidence Intervals for Simulation: Checking Independence and Normality

Chen, E. Jack; Kelton, W. David

doi:10.1177/0037549707086039

Cited by 32 publications

(20 citation statements)

References 14 publications

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“…Stopping rules are typically incorporated in CIPs that seek to return valid confidence intervals for data that may be dependent or nonnormal (Chen and Kelton 2007, Hoad et al 2009, Steiger and Wilson 2002, Tafazzoli et al 2011. Sequential ad hoc stopping rules are sometimes tailored to a specific set of simulation test models to provide better results.…”

Section: Introductionmentioning

confidence: 99%

Finite-Sample Performance of Absolute Precision Stopping Rules

Singham

Schruben

2012

INFORMS Journal on Computing

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A bsolute precision stopping rules are often used to determine the length of sequential experiments to estimate confidence intervals for simulated performance measures. Much is known about the asymptotic behavior of such procedures. In this paper, we introduce coverage contours to quantify the trade-offs in interval coverage, stopping times, and precision for finite-sample experiments using absolute precision rules. We use these contours to evaluate the coverage of a basic absolute precision stopping rule, and we show that this rule will lead to a bias in coverage even if all of the assumptions supporting the procedure are true. We define optimal stopping rules that deliver nominal coverage with the smallest expected number of observations. Contrary to previous asymptotic results that suggest decreasing the precision of the rule to approach nominal coverage in the limit, we find that it is optimal to increase the confidence coefficient used in the stopping rule, thus obtaining nominal coverage in a finite-sample experiment. If the simulation data are independent and identically normally distributed, we can calculate coverage contours analytically and find a stopping rule that is insensitive to the variance of the data while delivering at least nominal coverage for any precision value.

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Section: Introductionmentioning

confidence: 99%

Finite-Sample Performance of Absolute Precision Stopping Rules

Singham

Schruben

2012

INFORMS Journal on Computing

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“…A confidence interval for the mentioned above technique can be obtained using the corresponding theorem as can be seen in [5].…”

Section: Simulation Resultsmentioning

confidence: 99%

Simulation of the performance of CognitiveRadio Networks with unreliable servers

Zaghouani¹,

Sztrik²,

Uka³

2020

AMI

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This paper deals with a Cognitive Radio Network (CRN) which is modeled using a retrial queuing system with two finite-sources. This network includes two non-independent service units treating two types of users: Primary Users (PU) and Secondary Users (SU). The primary unit has priority queue (FIFO) and a second service unit contains an orbit both units are dedicated for the Primary Users and Secondary Users, respectively.The current work highlights the unreliability of the servers as we are assuming that both servers of this network are subject to random breakdowns and repairs. All the inter-event times in this CRN are either exponentially or non-exponentially distributed. The novelty of our investigation is to analyze the effect of several distributions (Gamma, Pareto, Log-normal, Hypo-Exponential and Hyper-Exponential) of the failure and repair times on the main performance measure of the system. By the help of simulation we show some interesting results concerning to sensitivity problems.

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“…has low power, these results provide some assurance that the quantile estimates obtained by the procedure of Chen and Kelton [11] appear to be normal. Moreover, the empirical experimental results of Chen and Kelton [21] indicate that for nonnormal data which pass the chi-square test of normality, the c.i. coverage is generally close to the nominal value.…”

Section: Var7mentioning

confidence: 99%

“…constructed from (independent) non-normal data that pass this chi-square normality test is still fairly accurate, i.e. the coverage is around the nominal value [21].…”

Section: Validation Of Normalitymentioning

confidence: 99%

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Some Procedures of Selecting the Best Designs with Respect to Quantile

Chen

2008

SIMULATION

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Indifference-zone selection procedures have been used to select a design with the minimum or maximum expected performance measure among a finite number of simulated designs. While there have been significant advancements in selection methodologies, the majority of the selection procedures are developed to process the selection when the performance measures are the mean of some output. In some situations, quantiles provide more suitable information. Quantiles are also more robust to outliers than the mean and standard deviation. Moreover, selection procedures are often derived based on the assumption that the input data are independent and identically distributed (i.i.d.) normal. In this paper we state and justify selection procedures when the ranking parameter is quantile. It is our intention for the quantile estimates to play the role of the i.i.d. normal observations that the original versions of selection procedures process. That is, we assume that our quantile estimates are approximately i.i.d. normal. We perform an empirical study of several stochastic processes to evaluate the performance of the procedure.

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A Procedure for Generating Batch-Means Confidence Intervals for Simulation: Checking Independence and Normality

Cited by 32 publications

References 14 publications

Finite-Sample Performance of Absolute Precision Stopping Rules

Finite-Sample Performance of Absolute Precision Stopping Rules

Simulation of the performance of CognitiveRadio Networks with unreliable servers

Some Procedures of Selecting the Best Designs with Respect to Quantile

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