evaluate_fit

Computes RMSE and log-likelihood scores for different mixture models compared to histogram data.

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

evaluate_fit function

<dl><dt>data</dt>
<dd>Numeric vector of observations.</dd>
<dt>methods</dt>
<dd>Named list of parameter sets (each with two components and a weight).</dd>
<dt>x_vals</dt>
<dd>Numeric vector. Evaluation grid.</dd></dl>

Arguments

Evaluate fit quality using RMSE and log-likelihood — evaluate_fit

<dl>

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


<dt>methods</dt>
<dd>Named list of parameter sets (each with two components and a weight).</dd>


<dt>x_vals</dt>
<dd>Numeric vector. Evaluation grid.</dd>

</dl>

evaluate_fit: Evaluate fit quality using RMSE and log-likelihood

Description

Usage

Value

Arguments