このページのリンク

<電子ブック>
Maximum Penalized Likelihood Estimation : Volume II: Regression / by Paul P. Eggermont, Vincent N. LaRiccia
(Springer Series in Statistics. ISSN:2197568X)

1st ed. 2009.
出版者 New York, NY : Springer New York : Imprint: Springer
出版年 2009
本文言語 英語
大きさ XX, 572 p : online resource
著者標目 *Eggermont, Paul P author
LaRiccia, Vincent N author
SpringerLink (Online service)
件 名 LCSH:Probabilities
LCSH:Statistics 
LCSH:Biometric identification
LCSH:Econometrics
LCSH:Signal processing
LCSH:Biometry
FREE:Probability Theory
FREE:Statistical Theory and Methods
FREE:Biometrics
FREE:Econometrics
FREE:Signal, Speech and Image Processing
FREE:Biostatistics
一般注記 Nonparametric Regression -- Smoothing Splines -- Kernel Estimators -- Sieves -- Local Polynomial Estimators -- Other Nonparametric Regression Problems -- Smoothing Parameter Selection -- Computing Nonparametric Estimators -- Kalman Filtering for Spline Smoothing -- Equivalent Kernels for Smoothing Splines -- Strong Approximation and Confidence Bands -- Nonparametric Regression in Action
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 statistics, operations research and applied mathematics, as well as for researchers and practitioners in the field. The present volume deals with nonparametric regression. The emphasis in this volume is on smoothing splines of arbitrary order, but other estimators (kernels, local and global polynomials) pass review as well. Smoothing splines and local polynomials are studied 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 that letting the innerproduct depend on the smoothing parameter opens up new possibilities. It leads to asymptotically equivalent reproducing kernel estimators (without qualifications), and thence, via uniform error bounds for kernel estimators, to uniform error bounds for smoothing splines and via strong approximations, to confidence bands for the unknown regression function. The reason for studying smoothing splines of arbitrary order is that one wants to use them for data analysis. Regarding the actual computation, the usual scheme based on spline interpolation is useful for cubic smoothing splines only. For splines of arbitrary order, the Kalman filter is the most important method, the intricacies of which are explained in full. The authors also discuss simulation results for smoothing splines and local and global polynomials for a variety of test problems as well as results on confidence bands for the unknown regression function based on undersmoothed quintic smoothing splines with remarkably good coverage probabilities. P.P.B. Eggermont and V.N. LaRiccia are with the Statistics Program of the Department of Food and Resource Economics in the College of Agriculture and Natural Resources at the University of Delaware, and the authors of Maximum Penalized Likelihood Estimation: Volume I: Density Estimation
HTTP:URL=https://doi.org/10.1007/b12285
目次/あらすじ

所蔵情報を非表示

電子ブック オンライン 電子ブック


Springer eBooks 9780387689029
電子リソース
EB00226737

書誌詳細を非表示

データ種別 電子ブック
分 類 LCC:QA273.A1-274.9
DC23:519.2
書誌ID 4000117932
ISBN 9780387689029

 類似資料