# Asymptotic Properties of Minimum S-Divergence Estimator for Discrete Models

arXiv: https://arxiv.org/abs/1403.6295 · HTML: https://mlsanity.com/paper/1403.6295/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Mar 2014.

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

- [rSDNet: Unified Robust Neural Learning against Label Noise and Adversarial Attacks](https://mlsanity.com/paper/2603.17628/) (2026)

## Abstract (arXiv)

Robust inference based on the minimization of statistical divergences has proved to be a useful alternative to the classical techniques based on maximum likelihood and related methods. Recently Ghosh et al. (2013) proposed a general class of divergence measures, namely the S-Divergence Family and discussed its usefulness in robust parametric estimation through some numerical illustrations. In this present paper, we develop the asymptotic properties of the proposed minimum S-Divergence estimators under discrete models.