# American Institute of Mathematical Sciences

January  2019, 4: 8 doi: 10.1186/s41546-019-0042-6

## Nonlinear regression without i.i.d. assumption

 UniDT, Shanghai, China

Published  November 2019

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.
Citation: Qing Xu, Xiaohua (Michael) Xuan. Nonlinear regression without i.i.d. assumption. Probability, Uncertainty and Quantitative Risk, 2019, 4 (0) : 8-. doi: 10.1186/s41546-019-0042-6
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