Discriminant analysis using the unweighted sum of binary variables: a comparison of model selection methods.
Discriminant analysis using the unweighted sum of binary variables: a comparison of model selection methods.
复制标题
使用二元变量未加权和的判别分析：模型选择方法的比较。
DOI：
10.1002/(sici)1097-0258(19971215)16:23
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发表时间：
1997
影响因子：
2
通讯作者：
Woolson,RF
中科院分区：
文献类型：
--
作者：
Langbehn,DR;Woolson,RF
许多临床决策规则等价于涉及二元变量未加权和(SBV)的线性判别函数。我们简要地考虑了这一限制的几何结构，然后提出了一些向前逐步选择SBV模型的方法。通过一个模拟研究，我们比较了这些方法在各种合理条件下的性能，结果表明，对于固定大小的模型的选择，没有一种方法是一致优越的。在相对方法性能中，通常重要的因素是样本大小与候选变量数量的比率以及数据的类别条件矩结构。最后，我们为SBV模型的构建提供了一些切实可行的策略。©1997 John Wiley&Sons，Ltd.
Many clinical decision‐making rules are equivalent to linear discriminant functions that involve the unweighted sum of binary variables (SBV). We briefly consider the geometry of this restriction and then propose a number of methods for forward stepwise selection of SBV models. Using a simulation study, we compare the performance of these methods under a wide range of plausible conditions and show that no single method is uniformly superior for selecting models of a fixed size. Factors of general importance in relative method performance are the ratio of sample size to the number of candidate variables and the class‐conditional moment structure of the data. We conclude by offering some practical strategies for SBV model construction. © 1997 John Wiley & Sons, Ltd.