Nonlinear Regression without i.i.d. Assumption
This addresses regression and machine learning problems where data may not be i.i.d., offering a solution for scenarios with dependent or non-identically distributed data, though it appears incremental as it builds on existing regression frameworks.
The paper tackles nonlinear regression problems without assuming independent and identically distributed data by proposing a mini-max problem and a numerical algorithm, which yields better results than traditional methods like least squares and other machine learning approaches.
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.