dc.contributor.author Samworth, Richard J dc.date.accessioned 2018-11-26T15:35:36Z dc.date.available 2018-11-26T15:35:36Z dc.date.issued 2018 dc.identifier.issn 0883-4237 dc.identifier.uri https://www.repository.cam.ac.uk/handle/1810/285984 dc.description.abstract 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. dc.publisher Institute of Mathematical Statistics dc.subject Log-concavity dc.subject maximum likelihood estimation dc.title Recent Progress in Log-Concave Density Estimation dc.type Article prism.endingPage 509 prism.issueIdentifier 4 prism.publicationDate 2018 prism.publicationName STATISTICAL SCIENCE prism.startingPage 493 prism.volume 33 dc.identifier.doi 10.17863/CAM.33307 dcterms.dateAccepted 2018-06-28 rioxxterms.versionofrecord 10.1214/18-STS666 rioxxterms.version AM rioxxterms.licenseref.uri http://www.rioxx.net/licenses/all-rights-reserved rioxxterms.licenseref.startdate 2018-11 dc.contributor.orcid Samworth, Richard [0000-0003-2426-4679] dc.identifier.eissn 2168-8745 rioxxterms.type Journal Article/Review pubs.funder-project-id Engineering and Physical Sciences Research Council (EP/J017213/1) pubs.funder-project-id Leverhulme Trust (PLP-2014-353) pubs.funder-project-id Engineering and Physical Sciences Research Council (EP/N031938/1) pubs.funder-project-id Engineering and Physical Sciences Research Council (EP/P031447/1) cam.issuedOnline 2018-11-01 cam.orpheus.success Thu Jan 30 10:54:03 GMT 2020 - The item has an open VoR version. rioxxterms.freetoread.startdate 2100-01-01
﻿