mock_lookup_alpha_beta

Provides a fallback estimation of alpha and beta from quantile ratios
using simple linear approximations.

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_lookup_alpha_beta function

<dl><dt>v_alpha</dt>
<dd>Quantile-based shape ratio.</dd>
<dt>v_beta</dt>
<dd>Quantile-based skewness ratio.</dd></dl>

Arguments

Mock lookup for alpha and beta (fallback) — mock_lookup_alpha_beta

<dl>

<dt>v_alpha</dt>
<dd>Quantile-based shape ratio.</dd>


<dt>v_beta</dt>
<dd>Quantile-based skewness ratio.</dd>

</dl>

mock_lookup_alpha_beta: Mock lookup for alpha and beta (fallback)

Description

Usage

Value

Arguments