HIERARCHICAL PARTITIONING
HIERARCHICAL PARTITIONING
复制标题
DOI：
10.2307/2684366
复制
发表时间：
1991-05-01
影响因子：
1.8
通讯作者：
SUTHERLAND, M
中科院分区：
文献类型：
--
作者：
CHEVAN, A;SUTHERLAND, M
回归方法的许多用户被这样的概念所吸引，即确定自变量的相对重要性将是有价值的。本文演示了一种基于层次结构的方法，该方法建立在以前通过增量分区分解R2的基础上。增量分区的标准方法是遵循许多可用顺序中的一个顺序。通过采用所有变量阶次的分层方法，得到了一个变量的平均独立贡献率，并得到了精确的划分结果。用大致相同的逻辑来划分变量的联合效应。该方法具有通用性，适用于所有回归方法，包括普通最小二乘法、Logistic回归、概率比特法和对数线性回归法。验证测试表明，该算法对数据中的关系敏感，而不是对所使用的统计模型所占的可变性比例敏感。
Many users of regression methods are attracted to the notion that it would be valuable to determine the relative importance of independent variables. This article demonstrates a method based on hierarchies that builds on previous efforts to decompose R2 through incremental partitioning. The standard method of incremental partitioning has been to follow one order among the many possible orders available. By taking a hierarchical approach in which all orders of variables are used, the average independent contribution of a variable is obtained and an exact partitioning results. Much the same logic is used to divide the joint effect of a variable. The method is general and applicable to all regression methods, including ordinary least squares, logistic, probit, and loglinear regression. A validation test demonstrates that the algorithm is sensitive to the relationships in the data rather than the proportion of variability accounted for by the statistical model used.