A tutorial on kernel density estimation and recent advances

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.

• Publication year

  2017

• Venue

  Biostatistics & Epidemiology

• Publication date

  2017-01-01

• Fields of study

  Mathematics, Computer Science

• Identifiers
  DOI 10.1080/24709360.2017.1396742arXiv 1704.03924
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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