A Distribution-Free Theory of Nonparametric Regression
The regression estimation problem has a long history. Already in 1632 Galileo Galilei used a procedure which can be interpreted as ?tting a linear relationship to contaminated observed data. Such ?tting of a line through a cloud of points is the classical linear regression problem. A solution of thi...
| Main Authors: | , , , |
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| Corporate Author: | |
| Format: | Electronic eBook |
| Language: | English |
| Published: |
New York, NY :
Springer New York,
2002.
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| Series: | Springer Series in Statistics,
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| Subjects: | |
| Online Access: | Full Text via HEAL-Link |
Table of Contents:
- Why Is Nonparametric Regression Important?
- How to Construct Nonparametric Regression Estimates?
- Lower Bounds
- Partitioning Estimates
- Kernel Estimates
- k-NN Estimates
- Splitting the Sample
- Cross-Validation
- Uniform Laws of Large Numbers
- Least Squares Estimates I: Consistency
- Least Squares Estimates II: Rate of Convergence
- Least Squares Estimates III: Complexity Regularization
- Consistency of Data-Dependent Partitioning Estimates
- Univariate Least Squares Spline Estimates
- Multivariate Least Squares Spline Estimates
- Neural Networks Estimates
- Radial Basis Function Networks
- Orthogonal Series Estimates
- Advanced Techniques from Empirical Process Theory
- Penalized Least Squares Estimates I: Consistency
- Penalized Least Squares Estimates II: Rate of Convergence
- Dimension Reduction Techniques
- Strong Consistency of Local Averaging Estimates
- Semirecursive Estimates
- Recursive Estimates
- Censored Observations
- Dependent Observations.