Estimation of the Extremal Index
Methods for objects of class "iwls"
Maximum likelihood estimation for the -gaps model
Confidence intervals for the extremal index for "kgaps"obje...
Information matrix test under the -gaps model
Iterated weighted least squares estimation of the extremal index
Internal exdex functions
Sliding and disjoint block maxima
Block length diagnostic for the semiparametric maxima estimator
Threshold and runs parameter diagnostic for the -gaps estim...
Threshold and runs parameter diagnostic for the -gaps estim...
Maximum likelihood estimation using left-censored inter-exceedances ti...
Confidence intervals for the extremal index for "dgaps"obje...
Information matrix test under the -gaps model
Statistics for the -gaps information matrix test
Methods for objects of class "dgaps"
Sufficient statistics for the left-censored inter-exceedances time mod...
exdex: Estimation of the Extremal Index
Statistics for the information matrix test
Methods for objects of class "kgaps"
Sufficient statistics for the -gaps model
Plot block length diagnostic for the semiparametric maxima estimator
Plot threshold and runs parameter diagnostic for the -gaps ...
Plot threshold and runs parameter diagnostic for the -gaps ...
Divides data into parts that contain no missing values
Semiparametric maxima estimator of the extremal index
Confidence intervals for the extremal index for "spm"object...
Methods for objects of class "spm"
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 and Fusek (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.
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