Estimation of the Extremal Index
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...
Confidence intervals for the extremal index for "dgaps"obje...
Statistics for the -gaps information matrix test
Information matrix test under the -gaps model
Methods for objects of class "dgaps"
Sufficient statistics for the left-censored inter-exceedances time mod...
Maximum likelihood estimation using left-censored inter-exceedances ti...
Internal exdex functions
exdex: Estimation of the Extremal Index
Methods for objects of class "iwls"
Iterated weighted least squares estimation of the extremal index
Confidence intervals for the extremal index for "kgaps"obje...
Statistics for the information matrix test
Information matrix test under the -gaps model
Methods for objects of class "kgaps"
Sufficient statistics for the -gaps model
Maximum likelihood estimation 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
Confidence intervals for the extremal index for "spm"object...
Methods for objects of class "spm"
Semiparametric maxima estimator 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 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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