A tutorial on kernel density estimation and recent advances

By Yen-Chi Chen. 2017

840 citations recorded by the source corpus.

ABSTRACT This tutorial provides a gentle introduction to kernel density estimation (KDE) and recent advances regarding confidence bands and geometric/topological features. We begin with a discussion of basic properties of KDE: the convergence rate under various metrics, density derivative estimation, and bandwidth selection. Then, we introduce common approaches to the construction of confidence intervals/bands, and we discuss how to handle bias. Next, we talk about recent advances in the inference of geometric and topological features of a density function using KDE. Finally, we illustrate how one can use KDE to estimate a cumulative distribution function and a receiver operating characteristic curve. We provide R implementations related to this tutorial at the end.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/0f62e20489b8535e26e8e5f281353d97c394a348

How researchers see this paper

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In Artificial Intelligence

1. Cecilia Clementi
2. Frank No'e
3. Alessandro Laio
4. Brooke E. Husic
5. Ping Luo
6. Xiaogang Wang
7. Kristina P. Sinaga
8. Miin-Shen Yang

In Computational Theory and Mathematics

1. Aldo Glielmo
2. Frank No'e
3. Cecilia Clementi
4. Brooke E. Husic

In Statistics and Probability

1. Cecilia Clementi
2. Aldo Glielmo
3. Frank No'e

Citations in the corpus

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