How to detect linear dependence on the copula level?
In many practical situations, the dependence between the quantities is linear or approximately linear. Knowing that the dependence is linear simplifies computations; so, is is desirable to detect linear dependencies. If we know the joint probability distribution, we can detect linear dependence by c...
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th-cmuir.6653943832-457142018-01-24T06:15:47Z How to detect linear dependence on the copula level? Vladik Kreinovich Hung T. Nguyen Songsak Sriboonchitta In many practical situations, the dependence between the quantities is linear or approximately linear. Knowing that the dependence is linear simplifies computations; so, is is desirable to detect linear dependencies. If we know the joint probability distribution, we can detect linear dependence by computing Pearson's correlation coefficient. In practice, we often have a copula instead of a full distribution; in this case, we face a problem of detecting linear dependence based on the copula. Also, distributions are often heavy-tailed, with infinite variances, in which case Pearson's formulas cannot be applied. In this paper, we show how to modify Pearson's formula so that it can be applied to copulas and to heavy-tailed distributions. © Springer International Publishing Switzerland 2014. 2018-01-24T06:15:47Z 2018-01-24T06:15:47Z 2014-01-01 Book Series 21945357 2-s2.0-84897890526 10.1007/978-3-319-03395-2_4 https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84897890526&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/45714 |
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In many practical situations, the dependence between the quantities is linear or approximately linear. Knowing that the dependence is linear simplifies computations; so, is is desirable to detect linear dependencies. If we know the joint probability distribution, we can detect linear dependence by computing Pearson's correlation coefficient. In practice, we often have a copula instead of a full distribution; in this case, we face a problem of detecting linear dependence based on the copula. Also, distributions are often heavy-tailed, with infinite variances, in which case Pearson's formulas cannot be applied. In this paper, we show how to modify Pearson's formula so that it can be applied to copulas and to heavy-tailed distributions. © Springer International Publishing Switzerland 2014. |
| format |
Book Series |
| author |
Vladik Kreinovich Hung T. Nguyen Songsak Sriboonchitta |
| spellingShingle |
Vladik Kreinovich Hung T. Nguyen Songsak Sriboonchitta How to detect linear dependence on the copula level? |
| author_facet |
Vladik Kreinovich Hung T. Nguyen Songsak Sriboonchitta |
| author_sort |
Vladik Kreinovich |
| title |
How to detect linear dependence on the copula level? |
| title_short |
How to detect linear dependence on the copula level? |
| title_full |
How to detect linear dependence on the copula level? |
| title_fullStr |
How to detect linear dependence on the copula level? |
| title_full_unstemmed |
How to detect linear dependence on the copula level? |
| title_sort |
how to detect linear dependence on the copula level? |
| publishDate |
2018 |
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https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84897890526&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/45714 |
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