exdex: Estimation of the Extremal Index

Performs frequentist inference for the extremal index of a stationary time series. Two types of methodology are used. One type is based on a model that relates the distribution of block maxima to the marginal distribution of series and leads to the semiparametric maxima estimators described in Northrop (2015) <doi:10.1007/s10687-015-0221-5> and Berghaus and Bucher (2018) <doi:10.1214/17-AOS1621>. Sliding block maxima are used to increase precision of estimation. A graphical block size diagnostic is provided. The other type of methodology uses a model for the distribution of threshold inter-exceedance times (Ferro and Segers (2003) <doi:10.1111/1467-9868.00401>). Three versions of this type of approach are provided: the iterated weight least squares approach of Suveges (2007) <doi:10.1007/s10687-007-0034-2>, the K-gaps model of Suveges and Davison (2010) <doi:10.1214/09-AOAS292> and a similar approach of Holesovsky, J. and Fusek, M. (2020) <doi:10.1007/s10687-020-00374-3> that we refer to as D-gaps. For the K-gaps and D-gaps models this package allows missing values in the data, can accommodate independent subsets of data, such as monthly or seasonal time series from different years, and can incorporate information from right-censored inter-exceedance times. Graphical diagnostics for the threshold level and the respective tuning parameters K and D are provided.

Package details

AuthorPaul J. Northrop [aut, cre, cph], Constantinos Christodoulides [aut, cph]
MaintainerPaul J. Northrop <p.northrop@ucl.ac.uk>
LicenseGPL (>= 2)
Version1.2.1
URL https://github.com/paulnorthrop/exdex https://paulnorthrop.github.io/exdex/
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("exdex")

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exdex documentation built on April 16, 2022, 9:05 a.m.