Subsampling weakly dependent times series and application to extremes

TL;DR

This paper extends subsampling methods to weakly dependent time series, analyzing the properties of estimators for distributions of converging and extreme statistics, with applications to extremes.

Contribution

It generalizes subsampling techniques from strongly mixing to weakly dependent processes using Doukhan and Louhichi's results.

Findings

Extended subsampling methods to weakly dependent series

Analyzed properties of estimators for extreme statistics

Applicable to distributions of converging statistics

Abstract

This paper provides extensions of the work on subsampling by Bertail et al. (2004) for strongly mixing case to weakly dependent case by application of the results of Doukhan and Louhichi (1999). We investigate properties of smooth and rough subsampling estimators for distributions of converging and extreme statistics when the underlying time series is {\eta} or {\lambda}-weakly dependent.