- Polynomial Regression
- Step Functions
- Regression splines 
- Smoothing splines 
- Local regression
- Generalized additive models

Nonlinear regression methods

It is easier to implement, interpret, and infer from the estimation of linear regression. However, one can rarely find a perfect linear relationship in the real world. Estimation using nonlinear regression is more difficult to implement, interpret, and infer from, but it gives us more predictive power by relaxing the assumption of linearity of the model.

Linear regression vs. nonlinear regression

Nonlinear regression models relax the linearity assumption of linear regression while trying to maintain its interpretability.

University of Michigan - Ann Arbor

- Linear regression
- Nonlinear regression

Types of regression

James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning (Vol. 112, pp. 3-7). New York: springer.

An Introduction to Statistical Learning with Applications in R

Linear regression is a linear approach to model a target prediction value based on independent variables.

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