Inference for copula modeling of discrete data: a cautionary tale and some facts
In this note, we elucidate some of the mathematical, statistical and epistemological issues involved in using copulas to model discrete data. We contrast the possible use of (nonparametric) copula methods versus the problematic use of parametric copula models. For the latter, we stress, among other...
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De Gruyter
2017-01-01
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| Series: | Dependence Modeling |
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| Online Access: | https://doi.org/10.1515/demo-2017-0008 |
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doaj-681e6297ff024b1692d74c08790eb1c22021-10-02T18:54:45ZengDe GruyterDependence Modeling2300-22982017-01-015112113210.1515/demo-2017-0008demo-2017-0008Inference for copula modeling of discrete data: a cautionary tale and some factsFaugeras Olivier P.0Toulouse School of Economics - Université Toulouse Capitole, Manufacture des Tabacs, Bureau MF319, 21 Allée de Brienne, 31000 Toulouse, FranceIn this note, we elucidate some of the mathematical, statistical and epistemological issues involved in using copulas to model discrete data. We contrast the possible use of (nonparametric) copula methods versus the problematic use of parametric copula models. For the latter, we stress, among other issues, the possibility of obtaining impossible models, arising from model misspecification or unidentifiability of the copula parameter.https://doi.org/10.1515/demo-2017-0008copuladiscrete dataparametric modelstatistical inferenceunidentifiability62a0162h2062h12 |
| collection |
DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
Faugeras Olivier P. |
| spellingShingle |
Faugeras Olivier P. Inference for copula modeling of discrete data: a cautionary tale and some facts Dependence Modeling copula discrete data parametric model statistical inference unidentifiability 62a01 62h20 62h12 |
| author_facet |
Faugeras Olivier P. |
| author_sort |
Faugeras Olivier P. |
| title |
Inference for copula modeling of discrete data: a cautionary tale and some facts |
| title_short |
Inference for copula modeling of discrete data: a cautionary tale and some facts |
| title_full |
Inference for copula modeling of discrete data: a cautionary tale and some facts |
| title_fullStr |
Inference for copula modeling of discrete data: a cautionary tale and some facts |
| title_full_unstemmed |
Inference for copula modeling of discrete data: a cautionary tale and some facts |
| title_sort |
inference for copula modeling of discrete data: a cautionary tale and some facts |
| publisher |
De Gruyter |
| series |
Dependence Modeling |
| issn |
2300-2298 |
| publishDate |
2017-01-01 |
| description |
In this note, we elucidate some of the mathematical, statistical and epistemological issues involved in using copulas to model discrete data. We contrast the possible use of (nonparametric) copula methods versus the problematic use of parametric copula models. For the latter, we stress, among other issues, the possibility of obtaining impossible models, arising from model misspecification or unidentifiability of the copula parameter. |
| topic |
copula discrete data parametric model statistical inference unidentifiability 62a01 62h20 62h12 |
| url |
https://doi.org/10.1515/demo-2017-0008 |
| work_keys_str_mv |
AT faugerasolivierp inferenceforcopulamodelingofdiscretedataacautionarytaleandsomefacts |
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1716848570280181760 |