Cópulas en geoestadística o lo que se puede hacer con coordenadas y estructuras de dependencia
It is common in geostatistics to use methods such as the variogram or the correlation coefficient to describe spatial dependence, and kriging to make interpolation and predictions, but these methods are sensitive to extreme values and are strongly influenced by marginal distribution of the random field....
- Autores:
-
Cruz Reyes, Danna Lesley
- Tipo de recurso:
- Fecha de publicación:
- 2013
- Institución:
- Universidad Santo Tomás
- Repositorio:
- Repositorio Institucional USTA
- Idioma:
- spa
- OAI Identifier:
- oai:repository.usta.edu.co:11634/39580
- Acceso en línea:
- https://revistas.usantotomas.edu.co/index.php/estadistica/article/view/1099
http://hdl.handle.net/11634/39580
- Palabra clave:
- Rights
- License
- http://purl.org/coar/access_right/c_abf2
| Summary: | It is common in geostatistics to use methods such as the variogram or the correlation coefficient to describe spatial dependence, and kriging to make interpolation and predictions, but these methods are sensitive to extreme values and are strongly influenced by marginal distribution of the random field. Hence they can lead to unreliable results. As an alternative to traditional models in geostatistics are considered the use of the copula functions. Copula is widely used in the finance and actuary fields and due to satisfactory results they started to be considered in other areas of application of statistical sciences. This work shows the effect of copulas as a tool that presents a geostatistical analysis under the range of quantiles and a dependence structure, considering models of spatial tendency, continuous and discrete marginal distributions and covariance functions. Three interpolation methods are shown: the first is the kriging indicator and disjunctive kriging, the second method is known as the simple kriging and the third method is a plug-in prediction and the generalization of the trans-Gaussian kriging, these methods are used based on the copula function due to the existing relationship between bivariate copulas and covariance indicators. Results are presented for a set of actual data in the city of Gomel that contains measurements of radioactive isotopes, consequence of the Chernobyl nuclear accident. Finally, discrete copulas are studied and applied to a set of simulated data, this allows an extension of the usual works of copulas in Geostatistics. |
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