On multivariate Gaussian copulas

Ivan Zezula

doi:10.1016/j.jspi.2009.05.039

On multivariate Gaussian copulas

Ivan Zezula^*

^*Corresponding author voor dit werk

Onderzoeksoutput: Article › Academic › peer review

46 Citaten (Scopus)

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Gaussian copulas are handy tool in many applications. However, when dimension of data is large, there are too many parameters to estimate. Use of special variance structure can facilitate the task. In many cases, especially when different data types are used. Pearson correlation is not a suitable measure of dependence. We study the properties of Kendall and Spearman correlation coefficients-which have better properties and are invariant under monotone transformations-used at the place of Pearson coefficients. Spearman correlation coefficient appears to be more suitable for use in such complex applications. (C) 2009 Elsevier B.V. All rights reserved.

Originele taal-2	English
Pagina's (van-tot)	3942-3946
Aantal pagina's	5
Tijdschrift	Journal of Statistical Planning and Inference
Volume	139
Nummer van het tijdschrift	11
DOI's	https://doi.org/10.1016/j.jspi.2009.05.039
Status	Published - 1-nov.-2009
Evenement	8th Tartu Conference on Multivariate Statistics/6th Conference on Multivariate Distributions with Fixed Marginals - Tartu, Estonia Duur: 26-jun.-2007 → 29-jun.-2007

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AB - Gaussian copulas are handy tool in many applications. However, when dimension of data is large, there are too many parameters to estimate. Use of special variance structure can facilitate the task. In many cases, especially when different data types are used. Pearson correlation is not a suitable measure of dependence. We study the properties of Kendall and Spearman correlation coefficients-which have better properties and are invariant under monotone transformations-used at the place of Pearson coefficients. Spearman correlation coefficient appears to be more suitable for use in such complex applications. (C) 2009 Elsevier B.V. All rights reserved.

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KW - Kendall

KW - Spearman

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On multivariate Gaussian copulas

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