Estimation of cluster functionals for regularly varying time series: sliding blocks estimators
Statistics Theory
2020-05-26 v1 Statistics Theory
Abstract
Cluster indices describe extremal behaviour of stationary time series. We consider their sliding blocks estimators. Using a modern theory of multivariate, regularly varying time series, we obtain central limit theorems under conditions that can be easily verified for a large class of models. In particular, we show that in the Peak over Threshold framework, sliding and disjoint blocks estimators have the same limiting variance.
Cite
@article{arxiv.2005.11378,
title = {Estimation of cluster functionals for regularly varying time series: sliding blocks estimators},
author = {Youssouph Cissokho and Rafal Kulik},
journal= {arXiv preprint arXiv:2005.11378},
year = {2020}
}
Comments
42 pages