# Robust Estimation of the Tail Index of a Single Parameter Pareto Distribution from Grouped Data

arXiv: https://arxiv.org/abs/2401.14593 · HTML: https://mlsanity.com/paper/2401.14593/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 22 references that are in the map.
- First posted on arXiv 26 Jan 2024.

## Builds on (most cited first, 5 of 5)

- [Truncated, Censored, and Actuarial Payment-type Moments for Robust Fitting of a Single-parameter Pareto Distribution](https://mlsanity.com/paper/2102.10154/) (2021)
- [Robust Estimation of Loss Models for Lognormal Insurance Payment Severity Data](https://mlsanity.com/paper/2103.02089/) (2021)
- [Robust Estimation of Loss Models for Truncated and Censored Severity Data](https://mlsanity.com/paper/2202.13000/) (2022)
- [Method of Winsorized Moments for Robust Fitting of Truncated and Censored Lognormal Distributions](https://mlsanity.com/paper/2204.02477/) (2022)
- [Modeling lower-truncated and right-censored insurance claims with an extension of the MBBEFD class](https://mlsanity.com/paper/2310.11471/) (2023)

## Abstract (arXiv)

Numerous robust estimators exist as alternatives to the maximum likelihood estimator (MLE) when a completely observed ground-up loss severity sample dataset is available. However, the options for robust alternatives to MLE become significantly limited when dealing with grouped loss severity data, with only a handful of methods like least squares, minimum Hellinger distance, and optimal bounded influence function available. This paper introduces a novel robust estimation technique, the Method of Truncated Moments (MTuM), specifically designed to estimate the tail index of a Pareto distribution from grouped data. Inferential justification of MTuM is established by employing the central limit theorem and validating them through a comprehensive simulation study.