# Parametric versus Semi and Nonparametric Regression Models

arXiv: https://arxiv.org/abs/1906.10221 · HTML: https://mlsanity.com/paper/1906.10221/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jun 2019.

## Built on by (most cited first, 2 of 2)

- [Agentic AI for autonomous anomaly management in complex systems](https://mlsanity.com/paper/2507.15676/) (2025)
- [Locally Near Optimal Piecewise Linear Regression in High Dimensions via Difference of Max-Affine Functions](https://mlsanity.com/paper/2605.06959/) (2026)

## Abstract (arXiv)

Three types of regression models researchers need to be familiar with and know the requirements of each: parametric, semiparametric and nonparametric regression models. The type of modeling used is based on how much information are available about the form of the relationship between response variable and explanatory variables, and the random error distribution. In this article, differences between models, common methods of estimation, robust estimation, and applications are introduced. The R code for all the graphs and analyses presented here, in this article, is available in the Appendix.