Nonlinear Regression without i.i.d. Assumption
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 outline a numerical algorithm. Such an algorithm can be applied in regression and machine learning problems, and yield better results than traditional regression and machine learning methods.
READ FULL TEXT