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A Distribution-Free Theory of Nonparametric Regression

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Cover of 'A Distribution-Free Theory of Nonparametric Regression'

Table of Contents

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    Book Overview
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    Chapter 1 Why Is Nonparametric Regression Important?
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    Chapter 2 How to Construct Nonparametric Regression Estimates?
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    Chapter 3 Lower Bounds
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    Chapter 4 Partitioning Estimates
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    Chapter 5 Kernel Estimates
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    Chapter 6 k-NN Estimates
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    Chapter 7 Splitting the Sample
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    Chapter 8 Cross-Validation
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    Chapter 9 Uniform Laws of Large Numbers
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    Chapter 10 Least Squares Estimates I: Consistency
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    Chapter 11 Least Squares Estimates II: Rate of Convergence
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    Chapter 12 Least Squares Estimates III: Complexity Regularization
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    Chapter 13 Consistency of Data-Dependent Partitioning Estimates
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    Chapter 14 Univariate Least Squares Spline Estimates
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    Chapter 15 Multivariate Least Squares Spline Estimates
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    Chapter 16 Neural Networks Estimates
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    Chapter 17 Radial Basis Function Networks
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    Chapter 18 Orthogonal Series Estimates
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    Chapter 19 Advanced Techniques from Empirical Process Theory
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    Chapter 20 Penalized Least Squares Estimates I: Consistency
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    Chapter 21 Penalized Least Squares Estimates II: Rate of Convergence
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    Chapter 22 Dimension Reduction Techniques
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    Chapter 23 Strong Consistency of Local Averaging Estimates
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    Chapter 24 Semirecursive Estimates
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    Chapter 25 Recursive Estimates
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    Chapter 26 Censored Observations
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    Chapter 27 Dependent Observations
Attention for Chapter 14: Univariate Least Squares Spline Estimates
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Chapter title
Univariate Least Squares Spline Estimates
Chapter number 14
Book title
A Distribution-Free Theory of Nonparametric Regression
Published by
Springer, New York, NY, January 2002
DOI 10.1007/0-387-22442-4_14
Book ISBNs
978-0-387-95441-7, 978-0-387-22442-8