Nantilus
Maximum penalized likelihood estimation : Volume II: regression
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 nonpara...
Enregistré dans:
| Auteurs principaux : | , |
|---|---|
| Format : | Livre |
| Langue : | anglais |
| Titre complet : | Maximum penalized likelihood estimation : Volume II: regression / by Vincent N. LaRiccia, Paul P. Eggermont |
| Publié : |
New York, NY :
Springer New York
, 2009 Springer e-books |
| Collection : | Springer series in statistics Mathematics and Statistics |
| Disponibilité : | L'accès complet au document est réservé aux usagers des établissements qui en ont fait l'acquisition |
| Contenu : | 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 |
| Sujets : | |
| Documents associés : | Regression:
Maximum penalized likelihood estimation |
| LEADER | 05215clm a2200733 4500 | ||
|---|---|---|---|
| 001 | PPN136302211 | ||
| 003 | http://www.sudoc.fr/136302211 | ||
| 005 | 20200916055400.0 | ||
| 010 | |a 978-0-387-68902-9 | ||
| 035 | |a (OCoLC)690564226 | ||
| 035 | |a Springer978-0-387-68902-9 | ||
| 035 | |a Springer-11649-978-0-387-68902-9 | ||
| 100 | |a 20090903d2009 k y0frey0103 ba | ||
| 101 | 0 | |a eng | |
| 102 | |a US | ||
| 105 | |a y a ||1y| | ||
| 135 | |a dr||||||||a|| | ||
| 181 | |6 z01 |c txt |2 rdacontent | ||
| 181 | 1 | |6 z01 |a i# |b xxxe## | |
| 182 | |6 z01 |c c |2 rdamedia | ||
| 182 | 1 | |6 z01 |a b | |
| 183 | 1 | |6 z01 |a ceb |2 rdacarrier | |
| 200 | 1 | |a Maximum penalized likelihood estimation |e Volume II: regression |f by Vincent N. LaRiccia, Paul P. Eggermont | |
| 210 | |a New York, NY |c Springer New York |c Springer e-books |d 2009 | ||
| 225 | 2 | |a Springer Series in Statistics |x 0172-7397 | |
| 230 | |a Données textuelles | ||
| 303 | |a L'impression du document génère 585 p. | ||
| 303 | |a Description d'après consultation du 06 juillet 2011 | ||
| 304 | |a Titre provenant de l'écran-titre | ||
| 305 | |a Numerisation de l'édition de : [s.l.] : Springer science + Business Media. 2009 | ||
| 310 | |a L'accès complet au document est réservé aux usagers des établissements qui en ont fait l'acquisition | ||
| 320 | |a Bibliogr. Index | ||
| 327 | 1 | |a Nonparametric Regression |a Smoothing Splines |a Kernel Estimators |a Sieves |a Local Polynomial Estimators |a Other Nonparametric Regression Problems |a Smoothing Parameter Selection |a Computing Nonparametric Estimators |a Kalman Filtering for Spline Smoothing |a Equivalent Kernels for Smoothing Splines |a Strong Approximation and Confidence Bands |a Nonparametric Regression in Action | |
| 330 | |a 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 | ||
| 337 | |a Nécessite un lecteur de fichier PDF | ||
| 410 | | | |0 013334581 |t Springer series in statistics |x 0172-7397 | |
| 410 | | | |t Mathematics and Statistics | |
| 452 | | | |0 136087590 |t Maximum penalized likelihood estimation |h volume II |i Regression |b Texte imprimé |f P.P.B. Eggermont, V.N. LaRiccia |c New York |n Springer |d cop. 2009 |p 1 vol. (XII-571 p.) |s Springer series in statistics |y 978-0-387-95268-0 | |
| 606 | |3 PPN027611361 |a Estimation, Théorie de l' |2 rameau | ||
| 610 | 1 | |a Mathematics | |
| 610 | 2 | |a Probability Theory and Stochastic Processes | |
| 610 | 2 | |a Statistical Theory and Methods | |
| 610 | 2 | |a Biometrics | |
| 610 | 2 | |a Econometrics | |
| 610 | 2 | |a Signal, Image and Speech Processing | |
| 610 | 2 | |a Biostatistics | |
| 615 | |a @Mathematics and Statistics |n 11649 |2 Springer | ||
| 676 | |a 519.2 |v 23 | ||
| 680 | |a QA273.A1-274.9 | ||
| 680 | |a QA274-274.9 | ||
| 686 | |a 35-XX |c 2010 |2 msc | ||
| 686 | |a 70-65 |c 2010 |2 msc | ||
| 686 | |a 44Axx |c 2010 |2 msc | ||
| 700 | 1 | |3 PPN059581859 |a LaRiccia |b V. N. |g Vincent N. |4 070 | |
| 701 | 1 | |a Eggermont |b Paul P |4 070 | |
| 702 | 1 | |a Eggermont |b Paul P |4 070 | |
| 801 | 3 | |a FR |b Abes |c 20180222 |g AFNOR | |
| 801 | 1 | |a DE |b Springer |c 20160105 |g AACR2 | |
| 915 | |5 441092208:639557139 |b 20673 | ||
| 930 | |5 441092208:639557139 |b 441092208 |a 62C453 |j u | ||
| 979 | |a CCFA | ||
| 991 | |5 441092208:639557139 |a exemplaire créé automatiquement par l'ABES | ||
| 997 | |a CCFA |b 20673 |d CMB |e BAP |s d |c 62C453 | ||
| 998 | |a 849316 | ||