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dc.contributor.authorSamworth, Richard J
dc.date.accessioned2018-11-26T15:35:36Z
dc.date.available2018-11-26T15:35:36Z
dc.date.issued2018
dc.identifier.issn0883-4237
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/285984
dc.description.abstractIn 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.publisherInstitute of Mathematical Statistics
dc.subjectLog-concavity
dc.subjectmaximum likelihood estimation
dc.titleRecent Progress in Log-Concave Density Estimation
dc.typeArticle
prism.endingPage509
prism.issueIdentifier4
prism.publicationDate2018
prism.publicationNameSTATISTICAL SCIENCE
prism.startingPage493
prism.volume33
dc.identifier.doi10.17863/CAM.33307
dcterms.dateAccepted2018-06-28
rioxxterms.versionofrecord10.1214/18-STS666
rioxxterms.versionAM
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.licenseref.startdate2018-11
dc.contributor.orcidSamworth, Richard [0000-0003-2426-4679]
dc.identifier.eissn2168-8745
rioxxterms.typeJournal Article/Review
pubs.funder-project-idEngineering and Physical Sciences Research Council (EP/J017213/1)
pubs.funder-project-idLeverhulme Trust (PLP-2014-353)
pubs.funder-project-idEngineering and Physical Sciences Research Council (EP/N031938/1)
pubs.funder-project-idEngineering and Physical Sciences Research Council (EP/P031447/1)
cam.issuedOnline2018-11-01
cam.orpheus.successThu Jan 30 10:54:03 GMT 2020 - The item has an open VoR version.
rioxxterms.freetoread.startdate2100-01-01


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