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Multivariate input modeling with Johnson distributions

Authors:

Paul M. Stanfield,

James R. Wilson,

Naomi F. Glasscock,

Jennie P. Psihogios, and

Joseph R. DavisAuthors Info & Claims

WSC '96: Proceedings of the 28th conference on Winter simulation

November 1996

Pages 1457 - 1464

https://doi.org/10.1145/256562.256990

Published: 08 November 1996 Publication History

Abstract

This paper introduces a new method for multivariate simulation input modeling based on the Johnson translation system of probability distributions. This technique matches the first four marginal moments and the correlation structure of a given set of sample data, allowing computationally efficient parameter estimation and random-vector generation. Applications of the technique in ergonomics and production scheduling are discussed. The proposed method is compared to traditional multivariate input-modeling techniques based on the Johnson translation system.

References

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Jagerman, D. L., and B. Melamed. 1992b. The transition and autocorrelation structure of TES processes, Part II: Special cases. Communication in Statistics-Stochastic Models 8 (3): 499-527.

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Melamed, B., J. R. Hill, and D. Goldsman. 1992. The TES methodology: Modeling empirical stationary time series. In Proceedings of the 1992 Winter Simulation Conference, ed. j. J. Swain, D. Goldsman, R. C. Crain, and j. R. Wilson, 135-144. Piscataway, New Jersey" Institute of Electrical and Electronics Engineers.

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Schreuder, H. T., and W. L. Harley. 1977. A useful bivariate distribution for describing stand structure of tree heights and diameters. Biometrics 33:471- 478.

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Swain, J. J., S. Venkatraman, and J. R. Wilson. 1988. Least-squares estimation of distribution functions in Johnson's translation system, journal of Statistical Computation and Simulation 29:271-297.

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Cited By

Zhao ZMu HYuen K(2024)Probability density function modelling and credible region construction for multivariate, asymmetric, and multimodal distributions of geotechnical dataStructural Safety10.1016/j.strusafe.2023.102429107(102429)Online publication date: Mar-2024
https://doi.org/10.1016/j.strusafe.2023.102429
Agakishiev IHärdle WKozmik KKopa MPetukhina A(2023)Multivariate Probabilistic Forecasting of Electricity Prices With Trading ApplicationsSSRN Electronic Journal10.2139/ssrn.4527675Online publication date: 2023
https://doi.org/10.2139/ssrn.4527675
Rodrigues JBenites YCancho VBalakrishnan NSuzuki A(2021)Bayesian meta-elliptical multivariate regression models with fixed marginals on unit intervalsCommunications in Statistics - Theory and Methods10.1080/03610926.2021.193353152:3(918-938)Online publication date: 26-Jun-2021
https://doi.org/10.1080/03610926.2021.1933531
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Information

Published In

cover image ACM Conferences

WSC '96: Proceedings of the 28th conference on Winter simulation

November 1996

1527 pages

ISBN:0780333837

Editors:
John M. Charnes
Univ. of Kansas, Lawrence
,
Douglas J. Morrice
Univ. of Texas, Austin
,
Daniel T. Brunner
Systemflow Simulations, Inc., Indianapolis, IN
,
James J. Swain
Univ. of Alabama, Huntsville, Indianapolis, IN

Sponsors

IIE: Institute of Industrial Engineers
INFORMS/CS: Computer Science TC
IEEE-SMCS: Systems, Man & Cybernetics Society
ASA: American Statistical Association
ACM: Association for Computing Machinery
SCS: Society for Computer Simulation
SIGSIM: ACM Special Interest Group on Simulation and Modeling
IEEE-CS: Computer Society
NIST: National Institute of Standards & Technology

Publisher

IEEE Computer Society

United States

Publication History

Published: 08 November 1996

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WSC90

Sponsor:

IIE
INFORMS/CS
IEEE-SMCS
ASA
ACM
SCS
SIGSIM
IEEE-CS
NIST

WSC90: 1990 Winter Simulation Conference

December 8 - 11, 1996

California, Coronado, USA

Acceptance Rates

WSC '96 Paper Acceptance Rate 128 of 187 submissions, 68%;

Overall Acceptance Rate 3,413 of 5,075 submissions, 67%

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Citations

Cited By

Zhao ZMu HYuen K(2024)Probability density function modelling and credible region construction for multivariate, asymmetric, and multimodal distributions of geotechnical dataStructural Safety10.1016/j.strusafe.2023.102429107(102429)Online publication date: Mar-2024
https://doi.org/10.1016/j.strusafe.2023.102429
Agakishiev IHärdle WKozmik KKopa MPetukhina A(2023)Multivariate Probabilistic Forecasting of Electricity Prices With Trading ApplicationsSSRN Electronic Journal10.2139/ssrn.4527675Online publication date: 2023
https://doi.org/10.2139/ssrn.4527675
Rodrigues JBenites YCancho VBalakrishnan NSuzuki A(2021)Bayesian meta-elliptical multivariate regression models with fixed marginals on unit intervalsCommunications in Statistics - Theory and Methods10.1080/03610926.2021.193353152:3(918-938)Online publication date: 26-Jun-2021
https://doi.org/10.1080/03610926.2021.1933531
Koutchadé OCarpentier AFemenia F(2020) Modeling Corners, Kinks, and Jumps in Crop Acreage Choices: Impacts of the EU Support to Protein Crops American Journal of Agricultural Economics10.1111/ajae.12152103:4(1502-1524)Online publication date: 28-Oct-2020
https://doi.org/10.1111/ajae.12152
PRYKHODKO NPRYKHODKO S(2018)Constructing the Nonlinear Regression Models on the Basis of Multivariate Normalizing Transformations.Èlektronnoe modelirovanie10.15407/emodel.40.06.10140:6(101-110)Online publication date: 17-Dec-2018
https://doi.org/10.15407/emodel.40.06.101
Koutchadé OCarpentier AFemenia F(2018)Modeling Heterogeneous Farm Responses to European Union Biofuel Support with a Random Parameter Multicrop ModelAmerican Journal of Agricultural Economics10.1093/ajae/aax091100:2(434-455)Online publication date: 11-Jan-2018
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Nuño Martinez ECutululis NSørensen P(2018)High dimensional dependence in power systems: A reviewRenewable and Sustainable Energy Reviews10.1016/j.rser.2018.05.05694(197-213)Online publication date: Oct-2018
https://doi.org/10.1016/j.rser.2018.05.056
Gao SLiu YCui HWang S(2017)Probabilistic Load Flow Analysis Using Randomized Quasi–Monte Carlo Sampling and Johnson TransformationJournal of Energy Engineering10.1061/(ASCE)EY.1943-7897.0000497143:6Online publication date: Dec-2017
https://doi.org/10.1061/(ASCE)EY.1943-7897.0000497
Zaman KMcDonald MMahadevan S(2013)Inclusion of correlation effects in model prediction under data uncertaintyProbabilistic Engineering Mechanics10.1016/j.probengmech.2013.05.00334(58-66)Online publication date: Oct-2013
https://doi.org/10.1016/j.probengmech.2013.05.003
Park CMatsumura THaftka RKim N(2012)Modeling the Effect of Structural Tests on Uncertainty in Estimated Failure Stress13th AIAA/ISSMO Multidisciplinary Analysis Optimization Conference10.2514/6.2010-9241Online publication date: 26-Jun-2012
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