# The Minimum S-Divergence Estimator under Continuous Models: The Basu-Lindsay Approach

arXiv: https://arxiv.org/abs/1408.1239 · HTML: https://mlsanity.com/paper/1408.1239/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Aug 2014.

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

- [rSDNet: Unified Robust Neural Learning against Label Noise and Adversarial Attacks](https://mlsanity.com/paper/2603.17628/) (2026)
- [No Unique Minimizer, No Problem: On the Consistency of Robust Neural Classifiers](https://mlsanity.com/paper/2608.08489/) (2026)

## Abstract (arXiv)

Robust inference based on the minimization of statistical divergences has proved to be a useful alternative to the classical maximum likelihood based techniques. Recently Ghosh et al. (2013) proposed a general class of divergence measures for robust statistical inference, named the S-Divergence Family. Ghosh (2014) discussed its asymptotic properties for the discrete model of densities. In the present paper, we develop the asymptotic properties of the proposed minimum S-Divergence estimators under continuous models. Here we use the Basu-Lindsay approach (1994) of smoothing the model densities that, unlike previous approaches, avoids much of the complications of the kernel bandwidth selection. Illustrations are presented to support the performance of the resulting estimators both in terms of efficiency and robustness through extensive simulation studies and real data examples.