Towards a better understanding of correlation

被引：16

作者：

Rayner, J. C. W. ^{[1
]}

Beh, Eric J. ^{[2
]}

机构：

[1] Univ Newcastle, Sch Math & Phys Sci, Newcastle, NSW 2308, Australia

[2] Univ Western Sydney, Sch Comp & Math, Penrith, NSW 1797, Australia

来源：

STATISTICA NEERLANDICA | 2009年 / 63卷 / 03期

关键词：

components; geometric linearity; orthonormal polynomials; structural linearity;

D O I：

10.1111/j.1467-9574.2009.00425.x

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

Correlation is often used to assess both independence and linearity in two dimensions, but is not well understood in higher dimensions. Here we take a closer look at these uses of correlation in two dimensions and how these assessments might be extended to higher dimensions. Our discussion will focus on the simple case of understanding the nature of correlation for association models for two- and three-way contingency tables, but can also be considered in a more general setting.

引用

页码：324 / 333

页数：10

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