# Robust Inference Using the Exponential-Polynomial Divergence

arXiv: https://arxiv.org/abs/2012.11735 · HTML: https://mlsanity.com/paper/2012.11735/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Dec 2020.

## Built on by (most cited first, 2 of 2)

- [A Unified Representation of Density-Power-Based Divergences Reducible to M-Estimation](https://mlsanity.com/paper/2501.16287/) (2025)
- [Unbiased Estimating Equation on Inverse Divergence and Its Conditions](https://mlsanity.com/paper/2404.16519/) (2024)

## Abstract (arXiv)

Density-based minimum divergence procedures represent popular techniques in parametric statistical inference. They combine strong robustness properties with high (sometimes full) asymptotic efficiency. Among density-based minimum distance procedures, the methods based on the Bregman-divergence have the attractive property that the empirical formulation of the divergence does not require the use of any non-parametric smoothing technique such as kernel density estimation. The methods based on the density power divergence (DPD) represent the current standard in this area of research. In this paper, we will present a more generalized divergence that subsumes the DPD as a special case and produces several new options providing better compromises between robustness and efficiency.