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ON COLLAPSING CATEGORIES IN TWO-WAY CONTINGENCY TABLES

机译:双向意外事件表中的收集类别

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The issue of collapsing categories of a contingency table's classification variables is well known and has been dealt with in the framework of classical models such as models of independence and association, canonical correlation and logistic regression. The most often used criterion is based on the homogeneity of the corresponding categories which was connected to association and correlation models by Goodman (1981a,b) and Gilula (1986), respectively. In this paper we relate homogeneity to a class of generalized association models, based on the f-divergence. The main issue raised in this paper is that the homogeneity and the structural criteria can not be contradictory. It is proved that collapsing among homogeneous categories does not affect the underlying structure of the table.
机译:列联表分类变量的折叠类别问题是众所周知的,并已在经典模型(例如独立性和关联性模型,规范相关性和逻辑回归模型)的框架中进行了处理。最常用的准则是基于相应类别的同质性,分别由Goodman(1981a,b)和Gilula(1986)连接到关联和关联模型。在本文中,我们基于f散度将同质性与一类广义关联模型联系起来。本文提出的主要问题是同质性和结构标准不能矛盾。事实证明,同类类别之间的折叠不会影响表格的基础结构。

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