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About this book
A comprehensive introduction to a wide variety of univariate and multivariate smoothing techniques for regression Smoothing and Regression: Approaches, Computation, and Application bridges the many gaps that exist among competing univariate and multivariate smoothing techniques. It introduces, describes, and in some cases compares a large number of the latest and most advanced techniques for regression modeling. Unlike many other volumes on this topic, which are highly technical and specialized, this book discusses all methods in light of both computational efficiency and their applicability for real data analysis. Using examples of applications from the biosciences, environmental sciences, engineering, and economics, as well as medical research and marketing, this volume addresses the theory, computation, and application of each approach. A number of the techniques discussed, such as smoothing under shape restrictions or of dependent data, are presented for the first time in book form. Special features of this book include:
* Comprehensive coverage of smoothing and regression with software hints and applications from a wide variety of disciplines
* A unified, easy-to-follow format
* Contributions from more than 25 leading researchers from around the world
* More than 150 illustrations also covering new graphical techniques important for exploratory data analysis and visualization of high-dimensional problems
* Extensive end-of-chapter references For professionals and aspiring professionals in statistics, applied mathematics, computer science, and econometrics, as well as for researchers in the applied and social sciences, Smoothing and Regression is a unique and important new resource destined to become one the most frequently consulted references in the field.
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Table of contents
- Cover
- Half Title page
- Title page
- Copyright page
- Dedication
- Contributors
- Foreword
- Preface
- Chapter 1: Spline Regression
- Chapter 2: Variance Estimation and Smoothing-Parameter Selection for Spline Regression
- Chapter 3: Kernel Regression
- Chapter 4: Variance Estimation and Bandwidth Selection for Kernel Regression
- Chapter 5: Spline and Kernel Regression Under Shape Restrictions
- Chapter 6: Spline and Kernel Regression for Dependent Data
- Chapter 7: Wavelets for Regression and Other Statistical Problems
- Chapter 8: Smoothing Methods for Discrete Data
- Chapter 9: Local Polynomial Fitting
- Chapter 10: Additive and Generalized Additive Models
- Chapter 11: Multivariate Spline Regression
- Chapter 12: Multivariate and Semiparametric Kernel Regression
- Chapter 13: Spatial-Process Estimates as Smoothers
- Chapter 14: Resampling Methods for Nonparametric Regression
- Chapter 15: Multidimensional Smoothing and Visualization
- Chapter 16: Projection Pursuit Regression
- Chapter 17: Sliced Inverse Regression
- Chapter 18: Dynamic and Semiparametric Models
- Chapter 19: Nonparametric Bayesian Bivariate Surface Estimation
- Index
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