# Maximum Correntropy Criterion Regression models with tending-to-zero scale parameters

arXiv: https://arxiv.org/abs/2110.12751 · HTML: https://mlsanity.com/paper/2110.12751/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 24 references that are in the map.
- First posted on arXiv 25 Oct 2021.

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

- [Consistency Analysis of an Empirical Minimum Error Entropy Algorithm](https://mlsanity.com/paper/1412.5272/) (2014)
- [A Statistical Learning Approach to Modal Regression](https://mlsanity.com/paper/1702.05960/) (2017)
- [Learning with Correntropy-induced Losses for Regression with Mixture of Symmetric Stable Noise](https://mlsanity.com/paper/1803.00183/) (2018)
- [M-type penalized splines with auxiliary scale estimation](https://mlsanity.com/paper/1906.08577/) (2019)
- [Robust penalized spline estimation with difference penalties](https://mlsanity.com/paper/2012.13295/) (2020)

## Abstract (arXiv)

Maximum correntropy criterion regression (MCCR) models have been well studied within the frame of statistical learning when the scale parameters take fixed values or go to infinity. This paper studies the MCCR models with tending-to-zero scale parameters. It is revealed that the optimal learning rate of MCCR models is O(n-1) in the asymptotic sense when the sample size n goes to infinity. In the case of finite samples, the performances on robustness of MCCR, Huber and the least square regression models are compared. The applications of these three methods on real data are also displayed.