Some Interpretative Tools for Non-Symmetrical Correspondence Analysis

被引：0
作者：
Eric J. Beh
Luigi D’Ambra
机构：
[1] University of Western Sydney,School of Computing and Mathematics
[2] Universita di Napoli “Federico II”,undefined
来源：
Journal of Classification | 2009年 / 26卷
关键词：
CATANOVA; Confidence circles; Goodman-Kruskal tau index; Linear constraints; Non-symmetrical correspondence analysis;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Non-symmetrical correspondence analysis (NSCA) is a very practical statistical technique for the identification of the structure of association between asymmetrically related categorical variables forming a contingency table. This paper considers some tools that can be used to numerically and graphically explore in detail the association between these variables and include the use of confidence regions, the establishment of the link between NSCA and the analysis of variance of categorical variables, and the effect of imposing linear constraints on a variable.
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页码：55 / 76
页数：21
相关论文
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