SPARSE MODELING OF CATEGORIAL EXPLANATORY VARIABLES
SPARSE MODELING OF CATEGORIAL EXPLANATORY VARIABLES
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DOI：
10.1214/10-aoas355
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发表时间：
2010-12-01
影响因子：
1.8
通讯作者：
Tutz, Gerhard
中科院分区：
文献类型：
--
作者：
Gertheiss, Jan;Tutz, Gerhard
回归分析中的收缩方法通常是为度量预测变量设计的。然而，在这篇文章中，收缩方法的类别预测。作为一个应用，我们考虑的数据从慕尼黑租金标准，其中，例如，城市地区被视为一个类别的预测。如果自变量是范畴的，则需要对通常的收缩过程进行一些修改。提出并研究了两种基于L-1罚的因子选择和聚类方法。第一种方法是为名义规模水平设计的，第二种方法是为有序预测变量设计的。除了将它们应用于慕尼黑租金标准，方法进行了说明和比较模拟研究。
Shrinking methods in regression analysis are usually designed for metric predictors. In this article, however, shrinkage methods for categorial predictors are proposed. As an application we consider data from the Munich rent standard, where, for example, urban districts are treated as a categorial predictor. If independent variables are categorial, some modifications to usual shrinking procedures are necessary. Two L-1-penalty based methods for factor selection and clustering of categories are presented and investigated. The first approach is designed for nominal scale levels, the second one for ordinal predictors. Besides applying them to the Munich rent standard, methods are illustrated and compared in simulation studies.