Year: 2021
Type: preprint
Abstract: 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 ${\mathcal{O}}(n^{-1})$ 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.
Source: arXiv (Cornell University)
Authors: Jing, Ying, Yang, Lianqiang
Institution: Anhui University
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
Award: G8073182337
OpenAlex ID: W3210431370