A survey of a hurdle model for heavy-tailed data based on the generalized lambda distribution
Applications
2019-01-04 v5
Abstract
In this survey we present an extensive research of the vast literature about the Generalized Lambda Distribution (GLD) and propose a hurdle, or two-way, model whose associated distribution is the GLD in order to meet the demand for a highly flexible model of heavy-tailed data with excess of zeros. We apply the developed models to a dataset consisting of yearly healthcare expenses, a typical example of heavy-tailed data with excess of zeros. The fitted models are compared with models based on the Generalised Pareto Distribution and it is established that the GLD models perform best.
Cite
@article{arxiv.1712.02183,
title = {A survey of a hurdle model for heavy-tailed data based on the generalized lambda distribution},
author = {Diego Marcondes and Cláudia Peixoto and Ana Carolina Maia},
journal= {arXiv preprint arXiv:1712.02183},
year = {2019}
}
Comments
37 pages, 8 figures