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An approximate method for generating symmetric random variables

Published: 01 November 1972 Publication History
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  • Abstract

    A method for generating values of continuous symmetric random variables that is relatively fast, requires essentially no computer memory, and is easy to use is developed. The method, which uses a uniform zero-one random number source, is based on the inverse function of the lambda distribution of Tukey. Since it approximates many of the continuous theoretical distributions and empirical distributions frequently used in simulations, the method should be useful to simulation practitioners.

    References

    [1]
    Filliben, J.J. Simple and robust linear estimation of the location parameter of a symmetric distribution. Princeton U., Princeton, N.J., 1969.
    [2]
    Hogg, R.V., and Craig, A.T. Introduction to Mathematical Statistics. Macmillan, New York, 1970.
    [3]
    Johnson, N.L., and Leone, F.C. Statistics and Experimental Design. Wiley, New York, 1964.
    [4]
    Naylor, T.H., Balintfy, J.L., Burdick, D.S., and Chu, K. Computer Simulation Techniques. Wiley, New York, 1966.
    [5]
    Tukey, J.W. Technical Report 36, Stat. Techniques Res. Group, Princeton U., Princeton, N.J., 1960.

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    • (2022)Modified information criterion for testing changes in generalized lambda distribution model based on confidence distributionCommunications for Statistical Applications and Methods10.29220/CSAM.2022.29.3.30129:3(301-317)Online publication date: 31-May-2022
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      Published In

      cover image Communications of the ACM
      Communications of the ACM  Volume 15, Issue 11
      Nov. 1972
      70 pages
      ISSN:0001-0782
      EISSN:1557-7317
      DOI:10.1145/355606
      Issue’s Table of Contents
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 01 November 1972
      Published in CACM Volume 15, Issue 11

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      Author Tags

      1. Monte Carlo
      2. approximations
      3. distribution
      4. moments
      5. probability
      6. random numbers
      7. random variables
      8. simulation
      9. statistics

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      • (2023)Applications of Burr III-Weibull quantile function in reliability analysisStatistical Theory and Related Fields10.1080/24754269.2023.22010967:4(296-308)Online publication date: 9-May-2023
      • (2022)Modified information criterion for testing changes in generalized lambda distribution model based on confidence distributionCommunications for Statistical Applications and Methods10.29220/CSAM.2022.29.3.30129:3(301-317)Online publication date: 31-May-2022
      • (2022)Generalization method of generating the continuous nested distributionsInternational Journal of Nonlinear Sciences and Numerical Simulation10.1515/ijnsns-2021-023124:4(1327-1353)Online publication date: 20-May-2022
      • (2022)On the goodness-of-fits of the generalized lambda distribution on high-frequency stock index returnsCogent Economics & Finance10.1080/23322039.2022.209576410:1Online publication date: 8-Jul-2022
      • (2021)Testing symmetry based on the extropy of record valuesJournal of Nonparametric Statistics10.1080/10485252.2021.1914338(1-22)Online publication date: 20-Apr-2021
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