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000825379 019__ $$a1017854177$$a1021234715$$a1032283403
000825379 020__ $$a9783319716886$$q(electronic book)
000825379 020__ $$a3319716883$$q(electronic book)
000825379 020__ $$z9783319716879
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000825379 0247_ $$a10.1007/978-3-319-71688-6$$2doi
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000825379 049__ $$aISEA
000825379 050_4 $$aQA353.K47
000825379 08204 $$a515/.9$$223
000825379 1001_ $$aGramacki, Artur,$$eauthor.
000825379 24510 $$aNonparametric kernel density estimation and its computational aspects /$$cArtur Gramacki.
000825379 264_1 $$aCham, Switzerland :$$bSpringer,$$c2018.
000825379 300__ $$a1 online resource.
000825379 336__ $$atext$$btxt$$2rdacontent
000825379 337__ $$acomputer$$bc$$2rdamedia
000825379 338__ $$aonline resource$$bcr$$2rdacarrier
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000825379 4901_ $$aStudies in big data,$$x2197-6503 ;$$vvolume 37
000825379 504__ $$aIncludes bibliographical references and index.
000825379 506__ $$aAccess limited to authorized users.
000825379 520__ $$aThis book describes computational problems related to kernel density estimation (KDE)? one of the most important and widely used data smoothing techniques. A very detailed description of novel FFT-based algorithms for both KDE computations and bandwidth selection are presented. The theory of KDE appears to have matured and is now well developed and understood. However, there is not much progress observed in terms of performance improvements. This book is an attempt to remedy this. The book primarily addresses researchers and advanced graduate or postgraduate students who are interested in KDE and its computational aspects. The book contains both some background and much more sophisticated material, hence also more experienced researchers in the KDE area may find it interesting. The presented material is richly illustrated with many numerical examples using both artificial and real datasets. Also, a number of practical applications related to KDE are presented.
000825379 588__ $$aOnline resource; title from PDF title page (viewed January 9, 2018).
000825379 650_0 $$aKernel functions.
000825379 650_0 $$aDigital filters (Mathematics)
000825379 77608 $$iPrint version:$$aGramacki, Artur.$$tNonparametric kernel density estimation and its computational aspects.$$dCham, Switzerland : Springer, 2018$$z3319716875$$z9783319716879$$w(OCoLC)1007929650
000825379 830_0 $$aStudies in big data ;$$vv. 37.
000825379 852__ $$bebk
000825379 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-319-71688-6$$zOnline Access$$91397441.1
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000825379 980__ $$aBIB
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