em_stable_mixture

Performs EM estimation using a user-defined parameter estimator and ECF frequencies.

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

em_stable_mixture function

<dl><dt>data</dt>
<dd>Numeric vector of observations.</dd>
<dt>u</dt>
<dd>Numeric vector of frequency values for ECF.</dd>
<dt>estimator_func</dt>
<dd>Function to estimate stable parameters.</dd>
<dt>max_iter</dt>
<dd>Integer. Maximum number of EM iterations.</dd>
<dt>epsilon</dt>
<dd>Numeric. Convergence threshold on log-likelihood.</dd></dl>

Arguments

EM algorithm for alpha-stable mixture using a custom estimator — em_stable_mixture

<dl>

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


<dt>u</dt>
<dd>Numeric vector of frequency values for ECF.</dd>


<dt>estimator_func</dt>
<dd>Function to estimate stable parameters.</dd>


<dt>max_iter</dt>
<dd>Integer. Maximum number of EM iterations.</dd>


<dt>epsilon</dt>
<dd>Numeric. Convergence threshold on log-likelihood.</dd>

</dl>

em_stable_mixture: EM algorithm for alpha-stable mixture using a custom estimator

Description

Usage

Value

Arguments