Modal Regression using Kernel Density Estimation: a Review

TL;DR

This review paper discusses recent developments in modal regression using kernel density estimation, highlighting models, estimators, properties, algorithms, and future research directions.

Contribution

It provides a comprehensive overview of modal regression with kernel density estimation, including theoretical properties and practical algorithms, which is a valuable resource for researchers.

Findings

Summarizes the models and estimators for modal regression.

Discusses asymptotic properties and bandwidth selection strategies.

Proposes future research directions in modal regression.

Abstract

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.