A nonlinear population Monte Carlo scheme for the Bayesian estimation of parameters of $\alpha$-stable distributions

TL;DR

This paper introduces a nonlinear population Monte Carlo method for Bayesian estimation of $oldsymbol{ extalpha}$-stable distribution parameters, outperforming existing techniques in accuracy and robustness, especially with limited data or challenging parameter ranges.

Contribution

The paper presents a novel NPMC algorithm for $oldsymbol{ extalpha}$-stable parameter estimation, demonstrating superior performance over traditional methods through simulations and real data applications.

Findings

NPMC outperforms existing methods in estimation accuracy.

Accurate estimates are achievable with few observations.

The method is effective across the entire range of $oldsymbol{ extalpha}$ values.

Abstract

The class of $\alpha$-stable distributions enjoys multiple practical applications in signal processing, finance, biology and other areas because it allows to describe interesting and complex data patterns, such as asymmetry or heavy tails, in contrast with the simpler and widely used Gaussian distribution. The density associated with a general $\alpha$-stable distribution cannot be obtained in closed form, which hinders the process of estimating its parameters. A nonlinear population Monte Carlo (NPMC) scheme is applied in order to approximate the posterior probability distribution of the parameters of an $\alpha$-stable random variable given a set of random realizations of the latter. The approximate posterior distribution is computed by way of an iterative algorithm and it consists of a collection of samples in the parameter space with associated nonlinearly-transformed importance weights. A numerical comparison of the main existing methods to estimate the $\alpha$-stable parameters is provided, including the traditional frequentist techniques as well as a Markov chain Monte Carlo (MCMC) and a likelihood-free Bayesian approach. It is shown by means of computer simulations that the NPMC method outperforms the existing techniques in terms of parameter estimation error and failure rate for the whole range of values of $\alpha$, including the smaller values for which most existing methods fail to work properly. Furthermore, it is shown that accurate parameter estimates can often be computed based on a low number of observations. Additionally, numerical results based on a set of real fish displacement data are provided.