# Modal Regression using Kernel Density Estimation: a Review

arXiv: https://arxiv.org/abs/1710.07004 · HTML: https://mlsanity.com/paper/1710.07004/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Oct 2017.

## Built on by (most cited first, 4 of 4)

- [Bagged k-Distance for Mode-Based Clustering Using the Probability of Localized Level Sets](https://mlsanity.com/paper/2210.09786/) (2022)
- [Ridge Estimation with Nonlinear Transformations](https://mlsanity.com/paper/2306.05722/) (2023)
- [A Monotone Single-Index Modal Regression Powered by Deep Neural Networks for Non-Gaussian Periodontal Data](https://mlsanity.com/paper/2505.02153/) (2025)
- [Unsupervised Learning of Density Estimates with Topological Optimization](https://mlsanity.com/paper/2512.08895/) (2025)

## Abstract (arXiv)

We review recent advances in modal regression studies using kernel density estimation. Modal regression is an alternative approach for investigating relationship between a response variable and its covariates. Specifically, modal regression summarizes the interactions between the response variable and covariates using the conditional mode or local modes. We first describe the underlying model of modal regression and its estimators based on kernel density estimation. We then review the asymptotic properties of the estimators and strategies for choosing the smoothing bandwidth. We also discuss useful algorithms and similar alternative approaches for modal regression, and propose future direction in this field.