Nonparametric regression penalizing deviations from additivity

DOI10.1214/009053604000001246zbMath1072.62031arXivmath/0507426OpenAlexW2952542717MaRDI QIDQ2569242

Yanyan Li

Publication date: 18 October 2005

Published in: The Annals of Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/math/0507426

zbMATH Keywords

regularizationAICparameter selectionmodel choiceadditive model

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Multivariate analysis (62H99)

Related Items

Local linear-additive estimation for multiple nonparametric regressions, Nonparametric regression penalizing deviations from additivity

Cites Work

• Unnamed Item
• Unnamed Item
• The existence and asymptotic properties of a backfitting projection algorithm under weak conditions
• The dimensionality reduction principle for generalized additive models
• Additive regression and other nonparametric models
• Optimal rates of convergence for nonparametric estimators
• Weak and strong uniform consistency of the kernel estimate of a density and its derivatives
• Local polynomial regression: Optimal kernels and asymptotic minimax efficiency
• Moderate deviations and large deviations for kernel density estimators
• A general projection framework for constrained smoothing.
• Optimal global rates of convergence for nonparametric regression
• Local linear regression smoothers and their minimax efficiencies
• Nonparametric regression penalizing deviations from additivity
• Finite-Sample Variance of Local Polynomials: Analysis and Solutions
• How Far Are Automatically Chosen Regression Smoothing Parameters From Their Optimum?
• An Optimization Interpretation of Integration and Back-Fitting Estimators for Separable Nonparametric Models
• Smoothing Parameter Selection in Nonparametric Regression Using an Improved Akaike Information Criterion
• Smooth Backfitting in Practice