A Distribution-Free Theory of Nonparametric Regression
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- Artikel-Nr.: 10355256
Beschreibung
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 * Semi-Recursive Estimates * Recursive Estimates * Censored Observations * Dependent Observations
Eigenschaften
Breite: | 161 |
Gewicht: | 1054 g |
Höhe: | 41 |
Länge: | 243 |
Seiten: | 650 |
Sprachen: | Englisch |
Autor: | Adam Krzyzak, Harro Walk, Laszlo Györfi, Michael Kohler |
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