plot_mixture

Plots the histogram of data and overlays the estimated mixture density and its components.

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

plot_mixture function

<dl><dt>data</dt>
<dd>Numeric vector of observations.</dd>
<dt>params1</dt>
<dd>Vector of parameters for component 1 (alpha, beta, gamma, delta).</dd>
<dt>params2</dt>
<dd>Vector of parameters for component 2.</dd>
<dt>w</dt>
<dd>Mixing weight for component 1.</dd>
<dt>label</dt>
<dd>Optional label for output file.</dd></dl>

Arguments

Plot mixture of two alpha-stable distributions — plot_mixture

<dl>

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


<dt>params1</dt>
<dd>Vector of parameters for component 1 (alpha, beta, gamma, delta).</dd>


<dt>params2</dt>
<dd>Vector of parameters for component 2.</dd>


<dt>w</dt>
<dd>Mixing weight for component 1.</dd>


<dt>label</dt>
<dd>Optional label for output file.</dd>

</dl>

plot_mixture: Plot mixture of two alpha-stable distributions

Description

Usage

Value

Arguments