| 書(shū)目名稱 | Combinatorial Methods in Density Estimation | | 編輯 | Luc Devroye,Gábor Lugosi | | 視頻video | http://file.papertrans.cn/230/229941/229941.mp4 | | 叢書(shū)名稱 | Springer Series in Statistics | | 圖書(shū)封面 |  | | 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 Gy?rfi, published the successful text, A Probabilistic Theory of Patter | | 出版日期 | Book 2001 | | 關(guān)鍵詞 | Density Estimation; Likelihood; Maxima; Probability theory; Variance | | 版次 | 1 | | doi | https://doi.org/10.1007/978-1-4613-0125-7 | | isbn_softcover | 978-1-4612-6527-6 | | isbn_ebook | 978-1-4613-0125-7Series ISSN 0172-7397 Series E-ISSN 2197-568X | | issn_series | 0172-7397 | | copyright | Springer-Verlag New York, Inc. 2001 |
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