Open Access
November 2018 Recent Progress in Log-Concave Density Estimation
Richard J. Samworth
Statist. Sci. 33(4): 493-509 (November 2018). DOI: 10.1214/18-STS666


In recent years, log-concave density estimation via maximum likelihood estimation has emerged as a fascinating alternative to traditional nonparametric smoothing techniques, such as kernel density estimation, which require the choice of one or more bandwidths. The purpose of this article is to describe some of the properties of the class of log-concave densities on $\mathbb{R}^{d}$ which make it so attractive from a statistical perspective, and to outline the latest methodological, theoretical and computational advances in the area.


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Richard J. Samworth. "Recent Progress in Log-Concave Density Estimation." Statist. Sci. 33 (4) 493 - 509, November 2018.


Published: November 2018
First available in Project Euclid: 29 November 2018

zbMATH: 07032826
MathSciNet: MR3881205
Digital Object Identifier: 10.1214/18-STS666

Keywords: Log-concavity , maximum likelihood estimation

Rights: Copyright © 2018 Institute of Mathematical Statistics

Vol.33 • No. 4 • November 2018
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