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Titlebook: Maximum Penalized Likelihood Estimation; Volume II: Regressio Vincent N. LaRiccia,Paul P.‘Eggermont Book 2009 Springer-Verlag New York 2009

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書(shū)目名稱Maximum Penalized Likelihood Estimation
副標(biāo)題Volume II: Regressio
編輯Vincent N. LaRiccia,Paul P.‘Eggermont
視頻videohttp://file.papertrans.cn/628/627913/627913.mp4
概述Fully develops the theory of convex minimization problems to obtain convergence rates.Includes simulation studies and analyses of classical data sets using fully automatic (data driven) procedures.Man
叢書(shū)名稱Springer Series in Statistics
圖書(shū)封面Titlebook: Maximum Penalized Likelihood Estimation; Volume II: Regressio Vincent N. LaRiccia,Paul P.‘Eggermont Book 2009 Springer-Verlag New York 2009
描述This is the second volume of a text on the theory and practice of maximum penalized likelihood estimation. It is intended for graduate students in s- tistics, operationsresearch, andappliedmathematics, aswellasresearchers and practitioners in the ?eld. The present volume was supposed to have a short chapter on nonparametric regression but was intended to deal mainly with inverse problems. However, the chapter on nonparametric regression kept growing to the point where it is now the only topic covered. Perhaps there will be a Volume III. It might even deal with inverse problems. But for now we are happy to have ?nished Volume II. The emphasis in this volume is on smoothing splines of arbitrary order, but other estimators (kernels, local and global polynomials) pass review as well. We study smoothing splines and local polynomials in the context of reproducing kernel Hilbert spaces. The connection between smoothing splines and reproducing kernels is of course well-known. The new twist is thatlettingtheinnerproductdependonthesmoothingparameteropensup new possibilities: It leads to asymptotically equivalent reproducing kernel estimators (without quali?cations) and thence, via uniform er
出版日期Book 2009
關(guān)鍵詞Confidence bands; Estimator; Kalman filter for smoothing splines; ; Likelihood; Local polynomials; Nonpara
版次1
doihttps://doi.org/10.1007/b12285
isbn_softcover978-1-4614-1712-5
isbn_ebook978-0-387-68902-9Series ISSN 0172-7397 Series E-ISSN 2197-568X
issn_series 0172-7397
copyrightSpringer-Verlag New York 2009
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Vincent N. LaRiccia,Paul P.‘EggermontFully develops the theory of convex minimization problems to obtain convergence rates.Includes simulation studies and analyses of classical data sets using fully automatic (data driven) procedures.Man
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Springer Series in Statisticshttp://image.papertrans.cn/m/image/627913.jpg
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978-1-4614-1712-5Springer-Verlag New York 2009
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Book 2009nection between smoothing splines and reproducing kernels is of course well-known. The new twist is thatlettingtheinnerproductdependonthesmoothingparameteropensup new possibilities: It leads to asymptotically equivalent reproducing kernel estimators (without quali?cations) and thence, via uniform er
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