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Random samples with known sample statistics: With application to variance reduction

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Russell C.H. ChengAuthors Info & Claims

WSC '83: Proceedings of the 15th conference on Winter Simulation - Volume 2

Pages 395 - 402

Published: 12 December 1983 Publication History

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Abstract

A number of sampling schemes are described which aim to improve the accuracy of estimators in simulation experiments. The schemes negatively correlate key sample statistics in pairs of runs or blocks of runs. Use is made of generators which produce random samples from certain distributions when sample statistics like the mean and dispersion are prescribed. Special cases considered include normal, inverse Gaussian and gamma generators. These can be used in their own right or as the basis of generators of other distributions. Guide lines are given which indicate the conditions under which the schemes might be effective.

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Cheng, RCH (1983a), Generation of inverse Gaussian variates with given sample mean and dispersion, MATH Report 83-1, Dept. of Mathematics, UWIST, Cardiff, Britain.

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Cheng, RCH (1983b), Generation of bivariate normal samples with given sample mean and covariance matrix, MATH Report, 83-2, Dept. of Mathematics, UWIST, Cardiff, Britain.

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Swain JFarrington PSadowski DSeila A(1994)Designing simulation experiments for evaluating manufacturing systemsProceedings of the 26th conference on Winter simulation10.5555/193201.193978(69-76)Online publication date: 11-Dec-1994
https://dl.acm.org/doi/10.5555/193201.193978
Farrington PSwain J(1993)Design of simulation experiments with manufacturing applicationsProceedings of the 25th conference on Winter simulation10.1145/256563.256588(69-75)Online publication date: 1-Dec-1993
https://dl.acm.org/doi/10.1145/256563.256588
Cheng R(1986)Variance reduction methodsProceedings of the 18th conference on Winter simulation10.1145/318242.318261(60-68)Online publication date: 1-Dec-1986
https://dl.acm.org/doi/10.1145/318242.318261
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Index Terms

Random samples with known sample statistics: With application to variance reduction
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Factorization methods
        Canonical correlation analysis
2. Mathematics of computing
  1. Probability and statistics

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WSC '83: Proceedings of the 15th conference on Winter Simulation - Volume 2

December 1983

703 pages

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IEEE Press

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Published: 12 December 1983

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Swain JFarrington PSadowski DSeila A(1994)Designing simulation experiments for evaluating manufacturing systemsProceedings of the 26th conference on Winter simulation10.5555/193201.193978(69-76)Online publication date: 11-Dec-1994
https://dl.acm.org/doi/10.5555/193201.193978
Farrington PSwain J(1993)Design of simulation experiments with manufacturing applicationsProceedings of the 25th conference on Winter simulation10.1145/256563.256588(69-75)Online publication date: 1-Dec-1993
https://dl.acm.org/doi/10.1145/256563.256588
Cheng R(1986)Variance reduction methodsProceedings of the 18th conference on Winter simulation10.1145/318242.318261(60-68)Online publication date: 1-Dec-1986
https://dl.acm.org/doi/10.1145/318242.318261
Nelson B(1985)A decomposition approach to variance reductionProceedings of the 17th conference on Winter simulation10.1145/21850.253059(23-32)Online publication date: 15-Dec-1985
https://dl.acm.org/doi/10.1145/21850.253059

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Collecting Random Samples from Facebook: An Efficient Heuristic for Sampling Large and Undirected Graphs via a Metropolis-Hastings Random Walk

On the Distribution of Random Samples from Finite Populations of Random Variables

A Novel Quota Sampling Algorithm for Generating Representative Random Samples given Small Sample Size

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