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Visualizing dependence in high-dimensional data: An application to S&P 500 constituent data

Marius Hofert and Wayne Oldford

Econometrics and Statistics, 2018, vol. 8, issue C, 161-183

Abstract: The notion of a zenpath and a zenplot is introduced to search and detect dependence in high-dimensional data for model building and statistical inference. By using any measure of dependence between two random variables (such as correlation, Spearman’s rho, Kendall’s tau, tail dependence etc.), a zenpath can construct paths through pairs of variables in different ways, which can then be laid out and displayed by a zenplot. The approach is illustrated by investigating tail dependence and model fit in constituent data of the S&P 500 during the financial crisis of 2007–2008. The corresponding Global Industry Classification Standard (GICS) sector information is also addressed.

Keywords: Zenpath; Zenplot; Detecting dependence; High dimensions; Graphical tools (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecosta:v:8:y:2018:i:c:p:161-183

DOI: 10.1016/j.ecosta.2017.03.007

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