# The modal age of Statistics

arXiv: https://arxiv.org/abs/1807.02789 · HTML: https://mlsanity.com/paper/1807.02789/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jul 2018.

## Built on by (most cited first, 9 of 9)

- [Moving Up the Cluster Tree with the Gradient Flow](https://mlsanity.com/paper/2109.08362/) (2021)
- [An Axiomatic Definition of Hierarchical Clustering](https://mlsanity.com/paper/2407.03574/) (2024)
- [The EM Perspective of Directional Mean Shift Algorithm](https://mlsanity.com/paper/2101.10058/) (2021)
- [Mode and Ridge Estimation in Euclidean and Directional Product Spaces: A Mean Shift Approach](https://mlsanity.com/paper/2110.08505/) (2021)
- [An Asymptotic Equivalence between the Mean-Shift Algorithm and the Cluster Tree](https://mlsanity.com/paper/2111.10298/) (2021)
- [Bump hunting through density curvature features](https://mlsanity.com/paper/2208.00174/) (2022)
- [Bagged k-Distance for Mode-Based Clustering Using the Probability of Localized Level Sets](https://mlsanity.com/paper/2210.09786/) (2022)
- [Bayesian taut splines for estimating the number of modes](https://mlsanity.com/paper/2307.05825/) (2023)
- [Testing-driven Variable Selection in Bayesian Modal Regression](https://mlsanity.com/paper/2510.23831/) (2025)

## Abstract (arXiv)

Recently, a number of statistical problems have found an unexpected solution by inspecting them through a "modal point of view". These include classical tasks such as clustering or regression. This has led to a renewed interest in estimation and inference for the mode. This paper offers an extensive survey of the traditional approaches to mode estimation and explores the consequences of applying this modern modal methodology to other, seemingly unrelated, fields.