A mixture model for unsupervised tail estimation

Autor:	Holden, Lars, Haug, Ola
Rok vydání:	2009
Předmět:	Statistics - Methodology
Druh dokumentu:	Working Paper
Popis:	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. Comment: Submitted to the Electronic Journal of Statistics (http://www.i-journals.org/ejs/) by the Institute of Mathematical Statistics (http://www.imstat.org)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/0902.4137 Zobrazit plný text záznamu View this record from Arxiv