Kernel estimation for additive models under dependence

DOI10.1016/0304-4149(93)90096-MzbMath0780.62031MaRDI QIDQ689169

Jangsun Baek, Thomas E. Wehrly

Publication date: 1993

Published in: Stochastic Processes and their Applications (Search for Journal in Brave)

zbMATH Keywords

predictionmean squared errortime seriesadditive modelsnonparametric regressiondependent datastrictly stationary processstrong mixing conditionasymptotically normalconditional mean functionadditive kernel estimatorsum of Nadaraya-Watson estimatorsunivariate optimal rate of convergence

Mathematics Subject Classification ID

Density estimation (62G07) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Asymptotic distribution theory in statistics (62E20) Asymptotic properties of nonparametric inference (62G20) Nonparametric statistical resampling methods (62G09)

Related Items (6)

Marginal integration \(M\)-estimators for additive models ⋮ Some automated methods of smoothing time-dependent data ⋮ Kernel Estimators for Additive Models with Dependent Observations from Random Fields ⋮ Wavelet-based robust estimation and variable selection in nonparametric additive models ⋮ Robust kernel estimators for additive models with dependent observations ⋮ Discontinuities in robust nonparametric regression with α-mixing dependence

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Linear smoothers and additive models
• Nonparametric regression estimation under mixing conditions
• Asymptotic distribution of robust estimators for nonparametric models from mixing processes
• Estimation de la transition de probabilité d'une chaîne de Markov Doeblin-recurrente. Étude du cas du processus autoregressif général d'ordre 1
• Additive regression and other nonparametric models
• Strong uniform convergence rates in robust nonparametric time series analysis and prediction: Kernel regression estimation from dependent observations
• Nonparametric curve estimation from time series
• Nonparametric function estimation involving time series
• A CENTRAL LIMIT THEOREM AND A STRONG MIXING CONDITION
• NONPARAMETRIC ESTIMATORS FOR TIME SERIES
• Uniform Consistency of Kernel Estimators of a Regression Function Under Generalized Conditions
• Nonparametric Density Estimation, Prediction, and Regression for Markov Sequences
• Estimating Optimal Transformations for Multiple Regression and Correlation
• Residual variance and residual pattern in nonlinear regression
• Flexible Parsimonious Smoothing and Additive Modeling

This page was built for publication: Kernel estimation for additive models under dependence