normalized_objective_omega

Computes the normalized gradient of the log-likelihood with respect to the omega parameter
using the latest values of alpha, beta, and delta stored in global vectors.

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

normalized_objective_omega function

<dl><dt>omega</dt>
<dd>Location parameter.</dd>
<dt>X1</dt>
<dd>Numeric vector of observations.</dd>
<dt>L_alpha</dt>
<dd>Vector of alpha values.</dd>
<dt>L_beta</dt>
<dd>Vector of beta values.</dd>
<dt>L_delta</dt>
<dd>Vector of delta values.</dd></dl>

Arguments

Normalized objective for omega parameter — normalized_objective_omega

<dl>

<dt>omega</dt>
<dd>Location parameter.</dd>


<dt>X1</dt>
<dd>Numeric vector of observations.</dd>


<dt>L_alpha</dt>
<dd>Vector of alpha values.</dd>


<dt>L_beta</dt>
<dd>Vector of beta values.</dd>


<dt>L_delta</dt>
<dd>Vector of delta values.</dd>

</dl>

normalized_objective_omega: Normalized objective for omega parameter

Description

Usage

Value

Arguments