Kernel Density-Based Linear Regression Estimate

DOI10.1080/03610926.2011.650269zbMath1279.62088OpenAlexW1989705587MaRDI QIDQ2873947

Weixin Yao, Zhibiao Zhao

Publication date: 28 January 2014

Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)

Full work available at URL: http://hdl.handle.net/2097/17210

zbMATH Keywords

EM algorithmlinear regressionmaximum likelihood estimatekernel density estimateleast squares estimate

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Density estimation (62G07) Linear regression; mixed models (62J05) Monte Carlo methods (65C05)

Related Items (12)

Semiparametric estimation for linear regression with symmetric errors ⋮ \(\sqrt{n}\)-consistent density estimation in semiparametric regression models ⋮ A multi-step kernel–based regression estimator that adapts to error distributions of unknown form ⋮ Iterative weighted estimation based on variance modelling in linear regression models ⋮ Semi-parametric estimation of the autoregressive parameter in non-Gaussian Ornstein–Uhlenbeck processes ⋮ Kernel density estimation for multiplicative distortion measurement regression models ⋮ Robust linear regression: A review and comparison ⋮ Determining the number of effective parameters in kernel density estimation ⋮ Kernel density estimation for partial linear multivariate responses models ⋮ Semiparametric mixture regression with unspecified error distributions ⋮ Kernel density regression ⋮ Adaptive estimation for varying coefficient models

Cites Work

• An adaptive estimation of MAVE
• On adaptive estimation
• Adaptive maximum likelihood estimators of a location parameter
• Weak and strong uniform consistency of the kernel estimate of a density and its derivatives
• On efficient estimation in regression models
• A NONPARAMETRIC REGRESSION ESTIMATOR THAT ADAPTS TO ERROR DISTRIBUTION OF UNKNOWN FORM
• Semiparametric inference with kernel likelihood
• Empirical likelihood ratio confidence intervals for a single functional

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