Description
Book SynopsisThis book 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.
Trade Review"…provides an accessible theoretical treatment of nonparametric regression." (
Journal of the American Statistical Association, December 2006)
"…I like this book, and recommend it to graduate students and researchers who plan to implement nonparametric models in their research." (Technometrics, November 2006)
"A very useful book clearly presenting basic concepts of nonparametric regression and applications to various real-life situations…highly recommended." (CHOICE, June 2006)
"…a practical introduction to nonparametric regression…" (Journal of Quality Technology, April 2006)
"…the presentation is very lucid and easy-to-follow…this book will highly be appreciated by students." (MAA Reviews, March 14, 2006)
"...deals concisely with the application of non-parametric regression to multidimensional data..." (Journal of Applied Statistics, 2007)
Table of ContentsPreface.
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.
