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.

中文翻译：

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没有iid假设的非线性回归

在本文中，我们考虑了一类非线性回归问题，而不假设它们是独立且均匀分布的。我们为非线性回归提出了相应的极大极小问题，并给出了数值算法。这种算法可以应用于回归和机器学习问题，并且比传统的最小二乘和机器学习方法产生更好的结果。