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Titlebook: Combinatorial Methods in Density Estimation; Luc Devroye,Gábor Lugosi Book 2001 Springer-Verlag New York, Inc. 2001 Density Estimation.Lik

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书目名称Combinatorial Methods in Density Estimation
编辑Luc Devroye,Gábor Lugosi
视频videohttp://file.papertrans.cn/230/229941/229941.mp4
丛书名称Springer Series in Statistics
图书封面Titlebook: Combinatorial Methods in Density Estimation;  Luc Devroye,Gábor Lugosi Book 2001 Springer-Verlag New York, Inc. 2001 Density Estimation.Lik
描述Density estimation has evolved enormously since the days of bar plots and histograms, but researchers and users are still struggling with the problem of the selection of the bin widths. This text explores a new paradigm for the data-based or automatic selection of the free parameters of density estimates in general so that the expected error is within a given constant multiple of the best possible error. The paradigm can be used in nearly all density estimates and for most model selection problems, both parametric and nonparametric. It is the first book on this topic. The text is intended for first-year graduate students in statistics and learning theory, and offers a host of opportunities for further research and thesis topics. Each chapter corresponds roughly to one lecture, and is supplemented with many classroom exercises. A one year course in probability theory at the level of Feller‘s Volume 1 should be more than adequate preparation. Gabor Lugosi is Professor at Universitat Pompeu Fabra in Barcelona, and Luc Debroye is Professor at McGill University in Montreal. In 1996, the authors, together with Lászlo Györfi, published the successful text, A Probabilistic Theory of Patter
出版日期Book 2001
关键词Density Estimation; Likelihood; Maxima; Probability theory; Variance
版次1
doihttps://doi.org/10.1007/978-1-4613-0125-7
isbn_softcover978-1-4612-6527-6
isbn_ebook978-1-4613-0125-7Series ISSN 0172-7397 Series E-ISSN 2197-568X
issn_series 0172-7397
copyrightSpringer-Verlag New York, Inc. 2001
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Bandwidth Choice with Superkernels,l—all densities in them must be infinitely many times continuously differentiate—this is an almost pointless exercise. Instead, we consider the ultimate kernels, those for which Φ(.) = .(1/√.). Clearly, .(1/√.) is the best possible rate, as Lemma 1 shows.
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H. Bauer,Ch. Korunka,M. Leodolterese densities cover ., that is, if these chosen densities are .. = {.1,…,..}, then . where .. = {. : ∫ |. − .| ≤ .}. The smallest such .. is called the . (or .) of . (and thus is a function of .), and log... is the . of .. .. is called a . of ..
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Skeleton Estimates,ese densities cover ., that is, if these chosen densities are .. = {.1,…,..}, then . where .. = {. : ∫ |. − .| ≤ .}. The smallest such .. is called the . (or .) of . (and thus is a function of .), and log... is the . of .. .. is called a . of ..
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Book 2001of the selection of the bin widths. This text explores a new paradigm for the data-based or automatic selection of the free parameters of density estimates in general so that the expected error is within a given constant multiple of the best possible error. The paradigm can be used in nearly all den
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