Résumé
Focussing on applications, this book covers a very broad range, including simple and complex univariate and multivariate density estimation, nonparametric regression estimation, categorical data smoothing, and applications of smoothing to other areas of statistics. It will thus be of particular interest to data analysts, as arguments generally proceed from actual data rather than statistical theory, while the "Background Material" sections will interest statisticians studying the field. Over 750 references allow researchers to find the original sources for more details, and the "Computational Issues" sections provide sources for statistical software that use the methods discussed. Each chapter includes exercises with a heavily computational focus based upon the data sets used in the book, making it equally suitable as a textbook for a course in smoothing.
Contents
- Introduction
- Simple Univariate Density Estimation
- Smoother Univariate Density Estimation
- Multivariate Density Estimation
- Nonparametric Regression
- Smoothing Ordered Categorical Data
- Further Applications of Smoothing
- Appendix A. Descriptions of the Data Sets
- Appendix B. More on Computational Issues
- References
- Author Index
- Subject Index
L'auteur - Jeffrey S. Simonoff
New York University, New York, NY, USA
Caractéristiques techniques
| PAPIER | |
| Éditeur(s) | Springer |
| Auteur(s) | Jeffrey S. Simonoff |
| Parution | 01/09/1996 |
| Nb. de pages | 338 |
| Format | 16 x 24 |
| Couverture | Relié |
| Poids | 660g |
| Intérieur | Noir et Blanc |
| EAN13 | 9780387947167 |
| ISBN13 | 978-0-387-94716-7 |
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