IDEAS home Printed from https://ideas.repec.org/a/inm/ormnsc/v44y1998i2p203-218.html
   My bibliography  Save this article

An Approximate Method for Sampling Correlated Random Variables from Partially-Specified Distributions

Author

Listed:
  • Philip M. Lurie

    (Institute for Defense Analyses, 1801 N. Beauregard Street, Alexandria, Virginia 22311-1772)

  • Matthew S. Goldberg

    (Institute for Defense Analyses, 1801 N. Beauregard Street, Alexandria, Virginia 22311-1772)

Abstract

This paper presents an algorithm for generating correlated vectors of random numbers. The user need not fully specify the joint distribution function; instead, the user "partially specifies" only the marginal distributions and the correlation matrix. The algorithm may be applied to any set of continuous, strictly increasing distribution functions; the marginal distributions need not all be of the same functional form. The correlation matrix is first checked for mathematical consistency (positive semi-definiteness), and adjusted if necessary. Then the correlated random vectors are generated using a combination of Cholesky decomposition and Gauss-Newton iteration. Applications are made to cost analysis, where correlations are often present between cost elements in a work breakdown structure.

Suggested Citation

  • Philip M. Lurie & Matthew S. Goldberg, 1998. "An Approximate Method for Sampling Correlated Random Variables from Partially-Specified Distributions," Management Science, INFORMS, vol. 44(2), pages 203-218, February.
    • Handle: RePEc:inm:ormnsc:v:44:y:1998:i:2:p:203-218
    DOI: 10.1287/mnsc.44.2.203
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/mnsc.44.2.203
    Download Restriction: no
    File URL: https://libkey.io/10.1287/mnsc.44.2.203?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Parrish, Rudolph S., 1990. "Generating random deviates from multivariate Pearson distributions," Computational Statistics & Data Analysis, Elsevier, vol. 9(3), pages 283-295, May.
    2. Allen Fleishman, 1978. "A method for simulating non-normal distributions," Psychometrika, Springer;The Psychometric Society, vol. 43(4), pages 521-532, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Schinckus, Christophe, 2015. "The valuation of social impact bonds: An introductory perspective with the Peterborough SIB," Research in International Business and Finance, Elsevier, vol. 35(C), pages 104-110.
    2. Max Auerswald & Morten Moshagen, 2015. "Generating Correlated, Non-normally Distributed Data Using a Non-linear Structural Model," Psychometrika, Springer;The Psychometric Society, vol. 80(4), pages 920-937, December.
    3. Mohan D. Pant & Todd C. Headrick, 2017. "Simulating Uniform- and Triangular- Based Double Power Method Distributions," Journal of Statistical and Econometric Methods, SCIENPRESS Ltd, vol. 6(1), pages 1-1.
    4. Schinckus, Christophe, 2018. "The valuation of social impact bonds: An introductory perspective with the Peterborough SIB," Research in International Business and Finance, Elsevier, vol. 45(C), pages 1-6.
    5. Pelagatti, Matteo M. & Sen, Pranab K., 2013. "Rank tests for short memory stationarity," Journal of Econometrics, Elsevier, vol. 172(1), pages 90-105.
    6. Emanuela Raffinetti & Pier Alda Ferrari, 2021. "A dependence measure flow tree through Monte Carlo simulations," Quality & Quantity: International Journal of Methodology, Springer, vol. 55(2), pages 467-496, April.
    7. Al-Subaihi, Ali A., 2004. "Simulating Correlated Multivariate Pseudorandom Numbers," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 9(i04).
    8. Yen Lee & David Kaplan, 2018. "Generating Multivariate Ordinal Data via Entropy Principles," Psychometrika, Springer;The Psychometric Society, vol. 83(1), pages 156-181, March.
    9. M Hashem Pesaran & Takashi Yamagata, 2012. "Testing CAPM with a Large Number of Assets," Discussion Papers 12/05, Department of Economics, University of York.
    10. Beasley, T. Mark & Zumbo, Bruno D., 2003. "Comparison of aligned Friedman rank and parametric methods for testing interactions in split-plot designs," Computational Statistics & Data Analysis, Elsevier, vol. 42(4), pages 569-593, April.
    11. Irmer, Julien Patrick & Klein, Andreas & Schermelleh-Engel, Karin, 2024. "A General Model-Implied Simulation-Based Power Estimation Method for Correctly and Misspecfied Models: Applications to Nonlinear and Linear Structural Equation Models," OSF Preprints pe5bj, Center for Open Science.
    12. Fei Gu & Hao Wu, 2016. "Raw Data Maximum Likelihood Estimation for Common Principal Component Models: A State Space Approach," Psychometrika, Springer;The Psychometric Society, vol. 81(3), pages 751-773, September.
    13. Pasquale Dolce & Cristina Davino & Domenico Vistocco, 2022. "Quantile composite-based path modeling: algorithms, properties and applications," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 16(4), pages 909-949, December.
    14. Peter Wludyka, 1999. "Two non-parametric, analysis-of-means-type tests for homogeneity of variances," Journal of Applied Statistics, Taylor & Francis Journals, vol. 26(2), pages 243-256.
    15. Yadira Pazmiño & José Juan de Felipe & Marc Vallbé & Franklin Cargua & Luis Quevedo, 2021. "Identification of a Set of Variables for the Classification of Páramo Soils Using a Nonparametric Model, Remote Sensing, and Organic Carbon," Sustainability, MDPI, vol. 13(16), pages 1-22, August.
    16. Po-Hsien Huang, 2017. "Asymptotics of AIC, BIC, and RMSEA for Model Selection in Structural Equation Modeling," Psychometrika, Springer;The Psychometric Society, vol. 82(2), pages 407-426, June.
    17. Ke-Hai Yuan & Peter Bentler, 2002. "On robusiness of the normal-theory based asymptotic distributions of three reliability coefficient estimates," Psychometrika, Springer;The Psychometric Society, vol. 67(2), pages 251-259, June.
    18. Vacca, Gianmarco & Zoia, Maria Grazia, 2019. "Kurtosis analysis in GARCH models with Gram–Charlier-like innovations," Economics Letters, Elsevier, vol. 183(C), pages 1-1.
    19. repec:jss:jstsof:09:i04 is not listed on IDEAS
    20. Ringle, C.M. & Götz, O & Wetzels, M.G.M. & Wilson, B, 2009. "On the Use of Formative Measurement Specifications in Structural Equation Modelling: A Monte Carlo Simulation Study to Compare Covariance-Based and Partial Least Squares Model Estimation Methodologies," Research Memorandum 014, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
    21. Brajendra C. Sutradhar & R. Prabhakar Rao, 2023. "Asymptotic Inferences in a Doubly-Semi-Parametric Linear Longitudinal Mixed Model," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 85(1), pages 214-247, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:inm:ormnsc:v:44:y:1998:i:2:p:203-218. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.