rlognGPD

This function simulates a lognormal-GPD mixture.

Estimation of a lognormal - Generalized Pareto mixture via the Expectation-Maximization algorithm. Computation of bootstrap standard errors is supported and performed via parallel computing. Functions for random number simulation and density evaluation are also available. For more details see Bee and Santi (2025) <doi:10.48550/arXiv.2505.22507>.

Marco Bee

lognGPD

Estimation of a Lognormal - Generalized Pareto Mixture

rlognGPD function

<dl><dt>n</dt>
<dd>positive integer: number of observations sampled.</dd>
<dt>p</dt>
<dd>real, 0&lt;p&lt;1: prior probability</dd>
<dt>mu</dt>
<dd>real: log-mean of the lognormal distribution.</dd>
<dt>sigma</dt>
<dd>positive real: log-standard deviation of the
lognormal distribution.</dd>
<dt>xi</dt>
<dd>real: shape parameter of the generalized Pareto distribution.</dd>
<dt>beta</dt>
<dd>positive real: scale parameter of the generalized Pareto distribution.</dd></dl>

Arguments

Simulation of the lognormal-GPD mixture — rlognGPD

<dl>

<dt>n</dt>
<dd>positive integer: number of observations sampled.</dd>


<dt>p</dt>
<dd>real, 0&lt;p&lt;1: prior probability</dd>


<dt>mu</dt>
<dd>real: log-mean of the lognormal distribution.</dd>


<dt>sigma</dt>
<dd>positive real: log-standard deviation of the
lognormal distribution.</dd>


<dt>xi</dt>
<dd>real: shape parameter of the generalized Pareto distribution.</dd>


<dt>beta</dt>
<dd>positive real: scale parameter of the generalized Pareto distribution.</dd>

</dl>

rlognGPD: Simulation of the lognormal-GPD mixture

Description

Usage

Value

Arguments

Examples