New Developments in Statistical Computing |
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Authors: | William J Kennedy Section Editor |
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Institution: | Sociology Department , State University of New York at Stony Brook , Stony Brook , NY , 11794 , USA |
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Abstract: | 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 R 2 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 log-linear 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. |
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Keywords: | Decomposition Regression |
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