plot.choose_b

<code>plot</code> method for objects inheriting from class <code>"choose_b"</code>,
returned from <code>choose_b</code>

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.

Paul Northrop

exdex

Estimation of the Extremal Index

Constantinos Christodoulides

plot.choose_b function

<dl><dt>x</dt>
<dd>an object of class <code>c("choose_b", "exdex")</code>, a result of a
call to <code>choose_b</code>.</dd>
<dt>y</dt>
<dd>Not used.</dd>
<dt>...</dt>
<dd>Additional arguments passed on to
<code><a href="/link/matplot?package=exdex&version=1.2.3" data-mini-rdoc="exdex::matplot">matplot</a></code> and/or <code><a href="/link/axis?package=exdex&version=1.2.3" data-mini-rdoc="exdex::axis">axis</a></code>.</dd>
<dt>estimator</dt>
<dd>Choice of estimator: <code>"N2015"</code> for Northrop (2015),
<code>"BB2018"</code> for Berghaus and Bucher (2018).
See <code>spm</code> for details.</dd>
<dt>maxima</dt>
<dd>Should the estimator be based on sliding or disjoint maxima?</dd></dl>

Arguments

See the examples in <code>choose_b</code>.

Examples

Plot block length diagnostic for the semiparametric maxima estimator — plot.choose_b

<dl>

<dt>x</dt>
<dd>an object of class <code>c("choose_b", "exdex")</code>, a result of a
call to <code>choose_b</code>.</dd>


<dt>y</dt>
<dd>Not used.</dd>


<dt>...</dt>
<dd>Additional arguments passed on to
<code><a href='https://rdrr.io/r/graphics/matplot.html'>matplot</a></code> and/or <code><a href='https://rdrr.io/r/graphics/axis.html'>axis</a></code>.</dd>


<dt>estimator</dt>
<dd>Choice of estimator: <code>"N2015"</code> for Northrop (2015),
<code>"BB2018"</code> for Berghaus and Bucher (2018).
See <code>spm</code> for details.</dd>


<dt>maxima</dt>
<dd>Should the estimator be based on sliding or disjoint maxima?</dd>

</dl>

See the examples in <code>choose_b</code>.

plot.choose_b: Plot block length diagnostic for the semiparametric maxima estimator

Description

Usage

Value

Arguments

Examples

Details

References

See Also