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

arXiv: https://arxiv.org/abs/2407.06522 · HTML: https://mlsanity.com/paper/2407.06522/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jul 2024.

## Built on by (most cited first, 1 of 1)

- [Coupled Entropy: A Goldilocks Generalization for Complex Systems](https://mlsanity.com/paper/2506.17229/) (2025)

## Abstract (arXiv)

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.