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Simulation of Twin Data Controlling Population Mean, Variance, Skewness and Kurtosis

Published online by Cambridge University Press:  01 August 2014

Joe C. Christian*
Affiliation:
Departments of Medical Genetics and Psychiatry, Indiana University School of Medicine, Indianapolis, Indiana, USA
Joan E. Bailey
Affiliation:
Departments of Medical Genetics and Psychiatry, Indiana University School of Medicine, Indianapolis, Indiana, USA
Mary M. Evans
Affiliation:
Departments of Medical Genetics and Psychiatry, Indiana University School of Medicine, Indianapolis, Indiana, USA
K. W. Kang
Affiliation:
Departments of Medical Genetics and Psychiatry, Indiana University School of Medicine, Indianapolis, Indiana, USA
James A. Norton Jr.
Affiliation:
Departments of Medical Genetics and Psychiatry, Indiana University School of Medicine, Indianapolis, Indiana, USA
P.L. Yu
Affiliation:
Departments of Medical Genetics and Psychiatry, Indiana University School of Medicine, Indianapolis, Indiana, USA
*Corresponding
Department of Medical Genetics, Indiana Univeristy Medical Center, 1100 West Michigan Street, Indianapolis, Indiana 46202, USA

Abstract

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A computer system for simulation of quantitative twin data is being developed. The capability is being built in to simulate distributions with known means, standard deviations, skewness and kurtosis.

Type
Brief Report
Copyright
Copyright © The International Society for Twin Studies 1977

References

Christian, J.C., Kang, K.W., Norton, J.A. Jr. 1974. Choice of an estimate of genetic variance from twin data. Am. J. Hum. Genet., 26: 154161.Google ScholarPubMed
Christian, J.C., Norton, J.A. Jr. 1977. A proposed test of the difference between the means of monozygotic and dizygotic twins. Acta Genet. Med. Gemellol., 26: 4953.CrossRefGoogle ScholarPubMed
Digital Equipment Corporation 1974. Basic Plus Language Manual Maynard, Massachusetts.Google Scholar
Ramberg, J.S., Schmeiser, B.W. 1974. An approximate method for generating asymmetric random variables. Communications of Association for Computing Machinery, 17: 7882.CrossRefGoogle Scholar
Schmeiser, B.W. 1971. A general algorithm for generating random variables. Masters Thesis, University of Iowa, Iowa City.Google Scholar
Schmeiser, B.W. 1976. Personal Communication. Department of Industrial Engineering and Operations Research, Southern Methodist University, Dallas, Texas 75275.Google Scholar
Stuart, F. 1970. Fortran Programming. New York: John Wiley and Sons.Google Scholar
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