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Tree-structured scale effects in binary and ordinal regression

机译：二进制和序数回归中的树结构尺度效应

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In binary and ordinal regression one can distinguish between a location component and a scaling component. While the former determines the location within the range of the response categories, the scaling indicates variance heterogeneity. In particular since it has been demonstrated that misleading effects can occur if one ignores the presence of a scaling component, it is important to account for potential scaling effects in the regression model, which is not possible in available recursive partitioning methods. The proposed recursive partitioning method yields two trees: one for the location and one for the scaling. They show in a simple interpretable way how variables interact to determine the binary or ordinal response. The developed algorithm controls for the global significance level and automatically selects the variables that have an impact on the response. The modeling approach is illustrated by several real-world applications.
机译：在二进制和序数回归中，可以区分位置组件和缩放组件。虽然前者确定响应类别范围内的位置，但缩放表示方差异质性。特别地，由于已经证明，如果忽略缩放组件的存在，则可能发生误导效果，因此在回归模型中考虑潜在的缩放效果，这是重要的，这在可用的递归分区方法中是不可能的。所提出的递归分区方法产生两棵树：一个用于位置，一个用于缩放的树木。它们以简单的解释方式显示变量如何相互作用以确定二进制或序数响应。发达的算法控制全局意义级别，并自动选择对响应产生影响的变量。若干现实应用示出了建模方法。

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