Nonlinear regression without i.i.d. assumption

Qing Xu; Xiaohua (Michael) Xuan

doi:10.1186/s41546-019-0042-6

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2019, Volume 4: 8. Doi: 10.1186/s41546-019-0042-6

This volume Previous Article Next Article Publisher Correction to: Probability, uncertainty and quantitative risk, volume 4

Nonlinear regression without i.i.d. assumption

Qing Xu and
Xiaohua (Michael) Xuan^,

UniDT, Shanghai, China

Xiaohua (Michael) Xuan, morrisxq@126.com

Published: July 2019

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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 yields better results than traditional least squares and machine learning methods.

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