Nonlinear Regression without i.i.d. Assumption
Methodology
2019-04-16 v2 Machine Learning
Abstract
In this paper, we consider a class of nonlinear regression problems without the assumption of being independent and identically distributed. We propose a correspondent mini-max problem for nonlinear regression and give a numerical algorithm. Such an algorithm can be applied in regression and machine learning problems, and yield better results than traditional least square and machine learning methods.
Cite
@article{arxiv.1811.09623,
title = {Nonlinear Regression without i.i.d. Assumption},
author = {Qing Xu and Xiaohua Xuan},
journal= {arXiv preprint arXiv:1811.09623},
year = {2019}
}