mock_gibbs_sampling

Performs a simplified Gibbs sampling procedure to estimate parameters of a two-component
alpha-stable mixture. Samples are drawn from prior distributions and evaluated using
log-likelihood. The best parameter set is selected based on likelihood.

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

mock_gibbs_sampling function

<dl><dt>data</dt>
<dd>Numeric vector of observations.</dd>
<dt>n_samples</dt>
<dd>Number of Gibbs samples to draw.</dd>
<dt>verbose</dt>
<dd>Logical, whether to print best log-likelihood.</dd></dl>

Arguments

Mock Gibbs sampling for alpha-stable mixture estimation — mock_gibbs_sampling

<dl>

<dt>data</dt>
<dd>Numeric vector of observations.</dd>


<dt>n_samples</dt>
<dd>Number of Gibbs samples to draw.</dd>


<dt>verbose</dt>
<dd>Logical, whether to print best log-likelihood.</dd>

</dl>

mock_gibbs_sampling: Mock Gibbs sampling for alpha-stable mixture estimation

Description

Usage

Value

Arguments