不同抽样算法对Sobol敏感性分析影响的研究

敏感性分析Sobol方法是其十分重要的一个分支.Sobol方法的数值解产生主要依赖蒙特卡洛方法.随机数的产生是蒙特卡洛方法的基础.随机数的生成最为主要的一类方法是伪随机数生成器,但由于伪随机数生成器所产生的随机数的均匀性不够好,因此会影响抽样质量,从而影响所计算的敏感性系数的收敛性与准确性.该文使用准随机和拉丁超立方体随机抽样器替代蒙特卡洛方法中的伪随机抽样器,并比较它们的抽样结果,以及最终所得的敏感性系数准确性及收敛性,最后发现准随机抽样相较另外两种抽样方式有明显优势.     

Robust dependence measure for detecting associations in large data set

In this paper, we proposed a new statistical dependency measure for two random vectors based on copula, called copula dependency coefficient (CDC). The CDC is proved to be robust to outliers and easy to be implemented. Especially, it is powerful and applicable to high-dimensional problems. All these properties make CDC practically important in related applications. Both experimental and application results show that CDC is a good robust dependence measure for association detecting.