Buch, Englisch, 650 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 1162 g
Buch, Englisch, 650 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 1162 g
Reihe: Springer Series in Statistics
ISBN: 978-0-387-95441-7
Verlag: Springer
This book provides a systematic in-depth analysis of nonparametric regression with random design. It covers almost all known estimates. The emphasis is on distribution-free properties of the estimates.
Zielgruppe
Research
Autoren/Hrsg.
Weitere Infos & Material
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.
