Jones, M. C. and Koch, I.
|DOI (Digital Object Identifier) Link:||http://doi.org/10.1023/A:1024270700807|
|Google Scholar:||Look up in Google Scholar|
There is often more structure in the way two random variables are associated than a single scalar dependence measure, such as correlation, can reflect. Local dependence functions such as that of Holland and Wang (1987) are, therefore, useful. However, it can be argued that estimated local dependence functions convey information that is too detailed to be easily interpretable. We seek to remedy this difficulty, and hence make local dependence a more readily interpretable practical tool, by introducing dependence maps. Via local permutation testing, dependence maps simplify the estimated local dependence structure between two variables by identifying regions of (significant) positive, (not significant) zero and (significant) negative local dependence. When viewed in conjunction with an estimate of the joint density, a comprehensive picture of the joint behaviour of the variables is provided. A little theory, many implementational details and several examples are given.
|Item Type:||Journal Article|
|Copyright Holders:||2003 Kluwer Academic Publishers|
|Keywords:||association; bivariate distribution; correlation; kernel smoothing; permutation test|
|Academic Unit/Department:||Faculty of Science, Technology, Engineering and Mathematics (STEM) > Mathematics and Statistics
Faculty of Science, Technology, Engineering and Mathematics (STEM)
|Depositing User:||Sarah Frain|
|Date Deposited:||25 Aug 2010 10:43|
|Last Modified:||04 Oct 2016 10:42|
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