Rolling Pin Method: Efficient General Method of Joint Probability Modeling
Rolling Pin Method: Efficient General Method of Joint Probability Modeling
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DOI：
10.1021/ie503584q
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发表时间：
2014-12
影响因子：
4.2
通讯作者：
T. M. Ahooyi;Jeffrey E. Arbogast;M. Soroush
中科院分区：
文献类型：
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作者：
T. M. Ahooyi;Jeffrey E. Arbogast;M. Soroush
本文提出了一种新的有效的方法来估计具有任意（非单调或单调）关系的连续随机变量的联合概率分布。由于该方法的骨干是一组单调化变换，“推出”的关系，该方法被命名为擀面杖方法。该方法允许一个估计联合概率分布时，实际的因果结构的属性是未知的或非常复杂的准确确定。一旦通过变换使关系单调化，则使用适当的参数copula函数来描述变换变量的联合分布。Copula函数允许用几个参数对变换变量的联合分布进行建模。单调化变换使标准参数Copula能够（i）捕获复杂的未知依赖结构，（ii）建模具有不同成对依赖关系的多元联合概率分布。
This paper presents a novel efficient method of estimating the joint probability distribution of continuous random variables with arbitrary (nonmonotonic or monotonic) relationships. As the backbone of the method is a set of monotonization transformations that “roll out” the relationships, the method is named the rolling pin method. The method allows one to estimate joint probability distributions when the actual causal structure of the attributes is unknown or extremely intricate to be determined accurately. Once the relationships are monotonized by the transformations, an appropriate parametric copula function is used to describe the joint distribution of the transformed variables. The copula function allows modeling the joint distribution of the transformed variables with a few parameters. The monotonization transformations empower standard parametric copulas to (i) capture complicated unknown dependence structures, (ii) model multivariate joint probability distributions with different pairwise depende...