Please use this identifier to cite or link to this item:
|Scopus||Web of Science®||Altmetric|
|Title:||Conditional expectation formulae for copulas|
Van Der Hoek, J.
|Citation:||Australian & New Zealand Journal of Statistics, 2008; 50(1):53-67|
|Publisher:||Blackwell Publ Ltd|
|Glenis J. Crane and John van der Hoek|
|Abstract:||Not only are copula functions joint distribution functions in their own right, they also provide a link between multivariate distributions and their lower-dimensional marginal distributions. Copulas have a structure that allows us to characterize all possible multivariate distributions, and therefore they have the potential to be a very useful statistical tool. Although copulas can be traced back to 1959, there is still much scope for new results, as most of the early work was theoretical rather than practical. We focus on simple practical tools based on conditional expectation, because such tools are not widely available. When dealing with data sets in which the dependence throughout the sample is variable, we suggest that copula-based regression curves may be more accurate predictors of specific outcomes than linear models. We derive simple conditional expectation formulae in terms of copulas and apply them to a combination of simulated and real data.|
|Keywords:||Archimedean copulas; conditional expectation; Farlie–Gumbel–Morgenstern copulas|
|Description:||© 2008 Australian Statistical Publishing Association Inc.|
|Appears in Collections:||Mathematical Sciences publications|
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.