A Distribution-Free Theory of Nonparametric Regression

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ISBN: 978-0-387-95441-7 (Print) 978-0-387-22442-8 (Online)

Table of contents (27 chapters)

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  1. Front Matter

    Pages i-xvi

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    Book Chapter

    Pages 1-17

    Why Is Nonparametric Regression Important?

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    Book Chapter

    Pages 18-30

    How to Construct Nonparametric Regression Estimates?

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    Book Chapter

    Pages 31-51

    Lower Bounds

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    Book Chapter

    Pages 52-69

    Partitioning Estimates

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    Book Chapter

    Pages 70-85

    Kernel Estimates

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    Book Chapter

    Pages 86-99

    k-NN Estimates

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    Book Chapter

    Pages 100-111

    Splitting the Sample

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    Book Chapter

    Pages 112-129

    Cross-Validation

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    Book Chapter

    Pages 130-157

    Uniform Laws of Large Numbers

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    Book Chapter

    Pages 158-182

    Least Squares Estimates I: Consistency

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    Book Chapter

    Pages 183-221

    Least Squares Estimates II: Rate of Convergence

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    Book Chapter

    Pages 222-234

    Least Squares Estimates III: Complexity Regularization

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    Book Chapter

    Pages 235-251

    Consistency of Data-Dependent Partitioning Estimates

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    Book Chapter

    Pages 252-282

    Univariate Least Squares Spline Estimates

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    Book Chapter

    Pages 283-296

    Multivariate Least Squares Spline Estimates

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    Book Chapter

    Pages 297-328

    Neural Networks Estimates

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    Book Chapter

    Pages 329-352

    Radial Basis Function Networks

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    Book Chapter

    Pages 353-379

    Orthogonal Series Estimates

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    Book Chapter

    Pages 380-406

    Advanced Techniques from Empirical Process Theory

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    Book Chapter

    Pages 407-432

    Penalized Least Squares Estimates I: Consistency

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