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  • 标题:Generating correlated random vector by Johnson system
  • 本地全文:下载
  • 作者:Qing Xiao
  • 期刊名称:Journal of Data Science
  • 印刷版ISSN:1680-743X
  • 电子版ISSN:1683-8602
  • 出版年度:2014
  • 卷号:12
  • 期号:2
  • 页码:217-234
  • 出版社:Tingmao Publish Company
  • 摘要:This paper aims to generate multivariate random vector with prescribedcorrelation matrix by Johnson system. The probability weighted moment(PWM) is employed to assess the parameters of Johnson system. By equat-ing the rst four PWMs of Johnson system with those of the target distri-bution, a system of equations solved for the parameters is established. Withsuitable initial values, solutions to the equations are obtained by the New-ton iteration procedure. To allow for the generation of random vector withprescribed correlation matrix, approaches to accommodate the dependencyare put forward. For the four transformation models of Johnson system,nine cases are addressed. Analytical formulae are derived to determine theequivalent correlation coecient in the standard normal space for six cases,the rest three ones are handled by an interpolation method. Finally, severalnumerical examples are given out to check the proposed method.
  • 关键词:correlation coecient; Johnson system; normal transformation;probability weighted moment.
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