makeReferenceMarginalDistribution

This gives the option of providing a set of reference marginal distributions to use for marginal transformation if the data's own marginal distribution is not appropriate (for instance if only data for which one variable is large is available, the marginal distributions of the other variables will not be represented by the available data). In such situations, the user can supply the full marginal information of the non-thresholded variables which are necessary to transform these variables correctly from the original margins to Gumbel/Laplace for estimation of dependence model parameters.

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

makeReferenceMarginalDistribution function

<dl><dt>x</dt>
<dd>output from migpd fit to the original data which does not represent at least one marginal distribution</dd>
<dt>r</dt>
<dd>output from migpd fit to the reference data which does represent the correct marginal distribution of the variable with incomplete representation in <code>x</code></dd>
<dt>whichNoChange</dt>
<dd>Margins which are not to use the supplied reference distribution <code>r</code> have numeric indices, giving column numbers in original dataframe, listed in <code>whichNoChange</code>.</dd></dl>

Arguments

Provide full marginal reference distribution for for maringal transformation — makeReferenceMarginalDistribution

<dl>

<dt>x</dt>
<dd>output from migpd fit to the original data which does not represent at least one marginal distribution</dd>


<dt>r</dt>
<dd>output from migpd fit to the reference data which does represent the correct marginal distribution of the variable with incomplete representation in <code>x</code></dd>


<dt>whichNoChange</dt>
<dd>Margins which are not to use the supplied reference distribution <code>r</code> have numeric indices, giving column numbers in original dataframe, listed in <code>whichNoChange</code>.</dd>

</dl>

makeReferenceMarginalDistribution: Provide full marginal reference distribution for for maringal transformation

Description

Usage

Value

Arguments