Details zur Publikation |
| Kategorie | Textpublikation |
| Referenztyp | Zeitschriften |
| DOI | 10.1007/s10651-021-00525-0 |
Lizenz  |
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| Titel (primär) | Modelling multivariate data using product copulas and minimum distance estimators: an exemplary application to ecological traits |
| Autor | Liebscher, E.; Taubert, F.; Waltschew, D.; Hetzer, J. |
| Quelle | Environmental and Ecological Statistics |
| Erscheinungsjahr | 2022 |
| Department | OESA |
| Band/Volume | 29 |
| Heft | 2 |
| Seite von | 315 |
| Seite bis | 338 |
| Sprache | englisch |
| Topic | T5 Future Landscapes |
| Keywords | Multivariate distributions; Copula; Plant traits; Trait distributions; Vegetation modelling |
| Abstract | Modelling and applying multivariate distributions is an important topic in ecology. In particular in plant ecology, the multidimensional nature of plant traits comes with challenges such as wide ranges in observations as well as correlations between several characteristics. In other disciplines (e.g., finances and economics), copulas have been proven as a valuable tool for modelling multivariate distributions. However, applications in ecology are still rarely used. Here, we present a copula-based methodology of fitting multivariate distributions to ecological data. We used product copula models to fit multidimensional plant traits, on example of observations from the global trait database TRY. The fitting procedure is split into two parts: fitting the marginal distributions and fitting the copula. We found that product copulas are well suited to model ecological data as they have the advantage of being asymmetric (similar to the observed data). Challenges in the fitting were mainly addressed to limited amount of data. In view of growing global databases, we conclude that copula modelling provides a great potential for ecological modelling. |
| dauerhafte UFZ-Verlinkung | https://www.ufz.de/index.php?en=20939&ufzPublicationIdentifier=25617 |
| Liebscher, E., Taubert, F., Waltschew, D., Hetzer, J. (2022): Modelling multivariate data using product copulas and minimum distance estimators: an exemplary application to ecological traits Environ. Ecol. Stat. 29 (2), 315 - 338 10.1007/s10651-021-00525-0 |
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