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The DOI matches its Crossref record; 83 of the 83 references it cites were found in a registry or an index.

A tutorial on kernel density estimation and recent advances

Open access
Chen, Yen-Chi·2017·Biostatistics & Epidemiology

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.

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Metadata

Authors
Chen, Y.-C.
Venue
Biostatistics & Epidemiology
Published
2017-01
Volume
1
Issue
1
Pages
161-187
ISSN
  • 2470-9360 (ISSN-L)
  • 2470-9379
OpenAlex
W2963647337
Cited by
830
References
83
FWCI
15.45
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: A tutorial on kernel density estimation and recent advances. https://app.cochat.ai/papers/10.1080/24709360.2017.1396742
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Chen, Y.-C. (2017). A tutorial on kernel density estimation and recent advances. Biostatistics & Epidemiology. https://doi.org/10.1080/24709360.2017.1396742

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.