The aim of this work is to develop a test to distinguish between heavy and super-heavy tailed probability distributions. These classes of distributions are relevant in areas such as telecommunications and insurance risk, among others. By heavy tailed distributions we mean probability distribution functions with polynomially decreasing upper tails (regularly varying tails). The term super-heavy is reserved for right tails decreasing to zero at a slower rate, such as logarithmic, or worse (slowly varying tails). Simulations are presented for several models and an application with telecommunications data is provided.

Additional Metadata
Keywords Estimation, Max-domain of attraction, Regular variation theory, Test of hypothesis
Persistent URL dx.doi.org/10.1016/j.jspi.2008.04.026, hdl.handle.net/1765/14326
Citation
Alves, I., de Haan, L.F.M., & Neves, C.. (2009). A test procedure for detecting super-heavy tails. Journal of Statistical Planning and Inference, 139(2), 213–227. doi:10.1016/j.jspi.2008.04.026