A mixture model for unsupervised tail estimation
Abstract
This paper proposes a new method to combine several densities such that each density dominates a separate part of a joint distribution. The method is fully unsupervised, i.e. the parameters in the densities and the thresholds are simultaneously estimated. The approach uses cdf functions in the mixing. This makes it easy to estimate parameters and the resulting density is smooth. Our method may be used both when the tails are heavier and lighter than the rest of the distribution. The presented model is compared with other published models and a very simple model using a univariate transformation.
Cite
@article{arxiv.0902.4137,
title = {A mixture model for unsupervised tail estimation},
author = {Lars Holden and Ola Haug},
journal= {arXiv preprint arXiv:0902.4137},
year = {2009}
}
Comments
Submitted to the Electronic Journal of Statistics (http://www.i-journals.org/ejs/) by the Institute of Mathematical Statistics (http://www.imstat.org)