Nonlinear Regression without i.i.d. Assumption

2018/11/23 by Qing Xu, Xu, Qing, Xiaohua Xuan +1 · 2 citations
Engineering · Mathematics · #Advanced Optimization Algorithms Research #Control Systems and Identification #FOS: Computer and information sciences #Machine Learning (cs.LG) #Methodology (stat.ME) #Sparse and Compressive Sensing Techniques

paper · pdf · doi:10.48550/arxiv.1811.09623

openalex publication_date 2018/11/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

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