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Introduction to Nonparametric Regression
John Wiley & Sons Inc (Hersteller)
978-0-471-77145-6 (ISBN)
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"Introduction to Nonparametric Regression" presents a complete but fundamental and readily accessible treatment of nonparametric regression, a subset of the larger area of nonparametric statistics. The explanations are presented in a user-friendly format and along with S-Plus and R subroutines in an effort to derive many of the real-world data and results. The overall theme of the book is to showcase the attractiveness and usefulness of nonparametric regression. In addition to discussing the usual kernel and spline methods, the book also briefly covers tree models.
KUNIO TAKEZAWA, PhD, is a Specific Research Scientist in the Department of Information Science and Technology at the National Agricultural Research Center, Japan. He is also an Associate Professor in the Cooperative Graduate School System at the Graduate School of Life and Environmental Sciences at the University of Tsukuba, Japan. Dr. Takezawa holds several patents in mathematics and is the recipient of a Research Award from the Japan Science and Technology Agency and a Thesis Award from the Japanese Agricultural Systems Society.
Preface. Acknowledgments. 1. Exordium. 2. Smoothing for Data with an Equispaced Predictor. 3. Nonparametric Regression for One--Dimensional Predictor. 4. Multidimensional Smoothing. 5. Nonparametric Regression with Predictors Represented as Distributions. 6. Smoothing of Histograms and Nonparametric Probability Density Functions. 7. Pattern Recognition. Appendix A: Creation and Applications of B--Spline Bases. Appendix B: R Objects. Appendix C: Further Readings. Index.
Erscheint lt. Verlag | 1.12.2005 |
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Verlagsort | New York |
Sprache | englisch |
Gewicht | 10 g |
Themenwelt | Mathematik / Informatik ► Mathematik |
ISBN-10 | 0-471-77145-7 / 0471771457 |
ISBN-13 | 978-0-471-77145-6 / 9780471771456 |
Zustand | Neuware |
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