Non-parametric methods for circular-circular and circular-linear
AbstractWe present a non-parametric approach for the estimation of the bivariate distribution of two circular variables and the modelling of the joint distribution of a circular and a linear variable. We combine nonparametric estimates of the marginal densities of the circular and linear components with the use of class of nonparametric copulas, known as empirical Bernstein copulas, to model the dependence structure. We derive the necessary conditions to obtain continuous distributions defined on the cylinder for the circular-linear model and on the torus for the circular-circular model. We illustrate these two approaches with two sets of real environmental data
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper provided by Universidad Carlos III, Departamento de Estadística y Econometría in its series Statistics and Econometrics Working Papers with number ws110704.
Date of creation: Apr 2011
Date of revision:
Contact details of provider:
Postal: C/ Madrid, 126 - 28903 GETAFE (MADRID)
Web page: http://www.uc3m.es/uc3m/dpto/DEE/departamento.html
More information through EDIRC
Bernstein polynomials; Circular distributions; Circular-Circular data; Circular-linear data; Copulas; Non-parametric estimation;
This paper has been announced in the following NEP Reports:
You can help add them by filling out this form.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ().
If references are entirely missing, you can add them using this form.