Independent Approximates provide a maximum likelihood estimate for heavy-tailed distributions

TL;DR

This paper introduces a novel method using n-tuple Independent Approximates and modified moments to effectively estimate heavy-tailed distributions like the generalized Pareto and Student's t, overcoming traditional challenges with infinite moments.

Contribution

It proposes a new estimation technique leveraging powers of distributions and n-tuple approximates, providing maximum likelihood estimates for heavy-tailed distributions.

Findings

Independent Approximates are maximum likelihood estimators for certain heavy-tailed distributions.

Modified moments from powers of distributions enable estimation despite infinite moments.

The method effectively estimates scale and shape parameters using least absolute deviation.

Abstract

Heavy-tailed distributions are infamously difficult to estimate because their moments tend to infinity as the shape of the tail decay increases. Nevertheless, this study shows the utilization of a modified group of moments for estimating a heavy-tailed distribution. These modified moments are determined from powers of the original distribution. The nth-power distribution is guaranteed to have finite moments up to n-1. Samples from the nth-power distribution are drawn from n-tuple Independent Approximates, which are the set of independent samples grouped into n-tuples and sub-selected to be approximately equal to each other. We show that Independent Approximates are a maximum likelihood estimator for the generalized Pareto and the Student's t distributions, which are members of the family of coupled exponential distributions. We use the first (original), second, and third power distributions to estimate their zeroth (geometric mean), first, and second power-moments respectively. In turn, these power-moments are used to estimate the scale and shape of the distributions. A least absolute deviation criteria is used to select the optimal set of Independent Approximates. Estimates using higher powers and moments are possible though the number of n-tuples that are approximately equal may be limited.