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04181nam a22005175i 4500 |
| 001 - CONTROL NUMBER |
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978-0-387-68902-9 |
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DE-He213 |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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20250710084008.0 |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION |
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cr nn 008mamaa |
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100301s2009 xxu| s |||| 0|eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9780387689029 |
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99780387689029 |
| 024 7# - OTHER STANDARD IDENTIFIER |
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| Standard number or code |
10.1007/b12285 |
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doi |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
LaRiccia, Vincent N. |
| Relator term |
author. |
| 245 10 - TITLE STATEMENT |
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| Title |
Maximum Penalized Likelihood Estimation |
| Medium |
[recurso electrónico] : |
| Remainder of title |
Volume II: Regression / |
| Statement of responsibility, etc. |
by Vincent N. LaRiccia, Paul P. Eggermont. |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
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| Place of production, publication, distribution, manufacture |
New York, NY : |
| Name of producer, publisher, distributor, manufacturer |
Springer New York, |
| Date of production, publication, distribution, manufacture, or copyright notice |
2009. |
| 300 ## - PHYSICAL DESCRIPTION |
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| Other physical details |
online resource. |
| 336 ## - CONTENT TYPE |
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| Content type term |
text |
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txt |
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rdacontent |
| 337 ## - MEDIA TYPE |
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computer |
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c |
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rdamedia |
| 338 ## - CARRIER TYPE |
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recurso en línea |
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cr |
| Source |
rdacarrier |
| 347 ## - DIGITAL FILE CHARACTERISTICS |
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| File type |
text file |
| Encoding format |
PDF |
| Source |
rda |
| 490 1# - SERIES STATEMENT |
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| Series statement |
Springer Series in Statistics, |
| International Standard Serial Number |
0172-7397 |
| 505 0# - FORMATTED CONTENTS NOTE |
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| Formatted contents note |
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. |
| 520 ## - SUMMARY, ETC. |
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| Summary, etc. |
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. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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STATISTICS. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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BIOMETRICS. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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STATISTICAL METHODS. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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COMPUTER SCIENCE |
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MATHEMATICS. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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MATHEMATICAL STATISTICS. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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ECONOMETRICS. |
| 650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
STATISTICS. |
| 650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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COMPUTATIONAL MATHEMATICS AND NUMERICAL ANALYSIS. |
| 650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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BIOSTATISTICS. |
| 650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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SIGNAL, IMAGE AND SPEECH PROCESSING. |
| 650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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ECONOMETRICS. |
| 650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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BIOMETRICS. |
| 650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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STATISTICAL THEORY AND METHODS. |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
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| Personal name |
Eggermont, Paul P. |
| Relator term |
author. |
| 710 2# - ADDED ENTRY--CORPORATE NAME |
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| Corporate name or jurisdiction name as entry element |
SpringerLink (Online service) |
| 773 0# - HOST ITEM ENTRY |
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| Title |
Springer eBooks |
| 776 08 - ADDITIONAL PHYSICAL FORM ENTRY |
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| Relationship information |
Printed edition: |
| International Standard Book Number |
9780387402673 |
| 830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE |
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| Uniform title |
Springer Series in Statistics, |
| International Standard Serial Number |
0172-7397 |
| 856 40 - ELECTRONIC LOCATION AND ACCESS |
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| Uniform Resource Identifier |
<a href="http://dx.doi.org/10.1007/b12285">http://dx.doi.org/10.1007/b12285</a> |
| Public note |
Ver el texto completo en las instalaciones del CICY |
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ZDB-2-SMA |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Source of classification or shelving scheme |
Dewey Decimal Classification |
| Koha item type |
Libros electrónicos |
