EMBoot

This function draws a bootstrap sample and uses it to estimate the parameters of a lognormal-Pareto mixture distribution. Since this is typically called by LPfitEM, see the help of LPfitEM for examples.

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

EMBoot function

<dl><dt>x</dt>
<dd>list: sequence of integers 1,...,K, where K is the mumber of datasets. Set x = 1 in case
of a single dataset.</dd>
<dt>x0</dt>
<dd>numerical vector (5x1): initial values of the parameters p,
\(\mu\), \(\sigma\), \(\xi\), \(\beta\).</dd>
<dt>y</dt>
<dd>numerical vector: observed sample.</dd>
<dt>maxiter</dt>
<dd>non-negative integer: maximum number of iterations of the EM algorithm.</dd></dl>

Arguments

Bootstrap standard errors for the MLEs of a lognormal-GPD mixture — EMBoot

<dl>

<dt>x</dt>
<dd>list: sequence of integers 1,...,K, where K is the mumber of datasets. Set x = 1 in case
of a single dataset.</dd>


<dt>x0</dt>
<dd>numerical vector (5x1): initial values of the parameters p,
\(\mu\), \(\sigma\), \(\xi\), \(\beta\).</dd>


<dt>y</dt>
<dd>numerical vector: observed sample.</dd>


<dt>maxiter</dt>
<dd>non-negative integer: maximum number of iterations of the EM algorithm.</dd>

</dl>

EMBoot: Bootstrap standard errors for the MLEs of a lognormal-GPD mixture

Description

Usage

Value

Arguments

Details