Stark D B, Reed J F
Department of Mathematical Sciences, University of Akron, OH 44325.
Comput Methods Programs Biomed. 1990 Mar-Apr;31(3-4):195-200. doi: 10.1016/0169-2607(90)90003-r.
Generating random variables from a specific distribution, whether symmetric or asymmetric, is a concern of investigators involved in Monte Carlo studies. Of particular interest to those concerned with robustness is the generation of contaminated symmetric distributions such as those used in the Princeton Robustness Study. A reliable composite uniform U(0,1) generator is described and algorithms for transforming U(0,1) to symmetric long-tailed and contaminated symmetric distributions are given. Goodness-of-fit tests and graphical illustrations demonstrate the adequacy of the empirical distributions.
从特定分布（无论是对称分布还是非对称分布）生成随机变量，是参与蒙特卡罗研究的研究人员所关注的问题。对于那些关注稳健性的人来说，特别感兴趣的是生成受污染的对称分布，例如普林斯顿稳健性研究中使用的分布。本文描述了一种可靠的复合均匀U(0,1)生成器，并给出了将U(0,1)转换为对称长尾分布和受污染对称分布的算法。拟合优度检验和图形说明证明了经验分布的充分性。
