A Distribution-Free Theory of Nonparametric Regression - Couverture souple

Couverture souple

ISBN 10 : 1475777027 ISBN 13 : 9781475777024

Editeur : Springer, 2014

Synopsis

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

Détails de l'édition

• Éditeur: Springer
• Date d'édition: 2014
• Langue: anglais
• ISBN 10: 1475777027
• ISBN 13: 9781475777024
• Reliure: Broché
• Nombre de pages: 668
• Éditeur: Gyorfi Laszlo, Kohler Michael, Krzyzak Adam
• Coordonnées du fabricant: KINESIS; https://www.also.com/ec/cms5/en_6000/6000/index.jsp; ; Friedrich-Karl-Str. 9; Hamburg; 22391; Allemagne