rMaxAR

Extreme Value random process generation.

Statistical extreme value modelling of threshold excesses, maxima
and multivariate extremes. Univariate models for threshold excesses and maxima
are the Generalised Pareto, and Generalised Extreme Value model respectively.
These models may be fitted by using maximum (optionally penalised-)likelihood,
or Bayesian estimation, and both classes of models may be fitted with covariates
in any/all model parameters. Model diagnostics support the fitting process.
Graphical output for visualising fitted models and return level estimates is
provided. For serially dependent sequences, the intervals declustering algorithm
of Ferro and Segers (2003) <doi:10.1111/1467-9868.00401> is provided, with
diagnostic support to aid selection of threshold and declustering horizon.
Multivariate modelling is performed via the conditional approach of Heffernan
and Tawn (2004) <doi:10.1111/j.1467-9868.2004.02050.x>, with graphical tools for
threshold selection and to diagnose estimation convergence.

Harry Southworth

texmex

Statistical Modelling of Extreme Values

Yiannis Papastathopoulos

Alec Stephenson

Stuart Coles

rMaxAR function

<dl><dt>n</dt>
<dd>Number of samples to generate.</dd>
<dt>theta</dt>
<dd>Parameter of the MAX AR process, takes values between 0 and 1.</dd></dl>

Arguments

Extreme Value random process generation. — rMaxAR

<dl>

<dt>n</dt>
<dd>Number of samples to generate.</dd>


<dt>theta</dt>
<dd>Parameter of the MAX AR process, takes values between 0 and 1.</dd>

</dl>

rMaxAR: Extreme Value random process generation.

Description

Usage

Arguments

Details

Examples