Correspondence Analysis Using the Cressie–Read Family of Divergence Statistics

IF 1.7 3区 数学 Q1 STATISTICS & PROBABILITY
Eric J. Beh, Rosaria Lombardo
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引用次数: 0

Abstract

The foundations of correspondence analysis rests with Pearson's chi-squared statistic. More recently, it has been shown that the Freeman–Tukey statistic plays an important role in correspondence analysis and confirmed the advantages of the Hellinger distance that have long been advocated in the literature. Pearson's and the Freeman–Tukey statistics are two of five commonly used special cases of the Cressie–Read family of divergence statistics. Therefore, this paper explores the features of correspondence analysis where its foundations lie with this family and shows that log-ratio analysis (an approach that has gained increasing attention in the correspondence analysis and compositional data analysis literature) and the method based on the Hellinger distance are special cases of this new framework.

Abstract Image

使用Cressie-Read分歧统计家族的对应分析
皮尔逊的卡方统计是对应分析的基础。最近的研究表明,弗里曼-图基统计量在对应分析中发挥了重要作用,并证实了文献中长期提倡的海灵格距离的优势。皮尔逊统计量和弗里曼-图基统计量是 Cressie-Read 发散统计量家族五个常用特例中的两个。因此,本文探讨了对应分析的特点,而对应分析的基础就在这一族中,并表明对数比率分析(一种在对应分析和组合数据分析文献中日益受到重视的方法)和基于海灵格距离的方法是这一新框架的特例。
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来源期刊
International Statistical Review
International Statistical Review 数学-统计学与概率论
CiteScore
4.30
自引率
5.00%
发文量
52
审稿时长
>12 weeks
期刊介绍: International Statistical Review is the flagship journal of the International Statistical Institute (ISI) and of its family of Associations. It publishes papers of broad and general interest in statistics and probability. The term Review is to be interpreted broadly. The types of papers that are suitable for publication include (but are not limited to) the following: reviews/surveys of significant developments in theory, methodology, statistical computing and graphics, statistical education, and application areas; tutorials on important topics; expository papers on emerging areas of research or application; papers describing new developments and/or challenges in relevant areas; papers addressing foundational issues; papers on the history of statistics and probability; white papers on topics of importance to the profession or society; and historical assessment of seminal papers in the field and their impact.
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