期刊名称：Electronic Journal of Applied Statistical Analysis
电子版ISSN：2070-5948
出版年度：2016
卷号：9
期号：1
页码：68-82
语种：English
出版社：University of Salento
摘要：Pareto processes are more suitable for time series with heavy tailed marginals than the classical gaussian. Here we consider the Lawrence-Lewis Pareto process. In particular, we analyze long-range and local dependence and compute some extremal measures. This will provide us some more diagnostic tools and new estimators for the autoregressive parameter of the process. Based on a simulation study we will see that the new methods may be good alternatives in what concerns robustness.
