compare_em_vs_em_gibbs

Compares log-likelihood and weight estimates between standard EM and EM with Gibbs sampling.

Provides various functions for parameter estimation of one-dimensional
stable distributions and their mixtures. It implements a diverse set of
estimation methods, including quantile-based approaches, regression methods
based on the empirical characteristic function (empirical, kernel, and
recursive), and maximum likelihood estimation. For mixture models, it provides
stochastic expectation–maximization (SEM) algorithms and Bayesian estimation
methods using sampling and importance sampling to overcome the long burn-in
period of Markov Chain Monte Carlo (MCMC) strategies. The package also includes
tools and statistical tests for analyzing whether a dataset follows a stable
distribution. Some of the implemented methods are described in
Hajjaji, O., Manou-Abi, S. M., and Slaoui, Y. (2024) <doi:10.1080/02664763.2024.2434627>.

Solym Manou-Abi

MixStable

Parameter Estimation for Stable Distributions and Their Mixtures

Adam Najib

Yousri Slaoui

compare_em_vs_em_gibbs function

<dl><dt>data</dt>
<dd>Numeric vector. Observed data.</dd>
<dt>n_runs</dt>
<dd>Integer. Number of runs for comparison.</dd></dl>

Arguments

Compare standard EM and EM with Gibbs sampling using kernel ECF — compare_em_vs_em_gibbs

<dl>

<dt>data</dt>
<dd>Numeric vector. Observed data.</dd>


<dt>n_runs</dt>
<dd>Integer. Number of runs for comparison.</dd>

</dl>

compare_em_vs_em_gibbs: Compare standard EM and EM with Gibbs sampling using kernel ECF

Description

Usage

Value

Arguments