Integrated Estimation of Functional-Coefficient Regression Models with Different Smoothing Variables

DOI10.1080/03610920600672260zbMath1118.62044OpenAlexW1992995426MaRDI QIDQ5484688

Ri-quan Zhang, Guo-Ying Li

Publication date: 21 August 2006

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

Full work available at URL: https://doi.org/10.1080/03610920600672260

zbMATH Keywords

asymptotic normalitylocal linear methoddifferent smoothing variablesintegrated estimatefunctional coefficient regression models

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Asymptotic properties of nonparametric inference (62G20)

Related Items

Tests for nonparametric parts on partially linear single index models ⋮ Empirical likelihood for generalized functional-coefficient regression models with multiple smoothing variables under right censoring data ⋮ Multiple-index varying-coefficient models for longitudinal data ⋮ Averaged and integrated estimations of varying-coefficient regression models with dependent observations ⋮ Efficient estimation of adaptive varying-coefficient partially linear regression model ⋮ Generalized likelihood ratio test for varying-coefficient models with different smoothing variables ⋮ Varying-coefficient single-index model

Cites Work

• Weak and strong uniform consistency of the kernel estimate of a density and its derivatives
• Statistical estimation in varying coefficient models
• Nonparametric smoothing estimates of time-varying coefficient models with longitudinal data
• Smoothing Spline Estimation for Varying Coefficient Models With Repeatedly Measured Dependent Variables
• Asymptotic Confidence Regions for Kernel Smoothing of a Varying-Coefficient Model with Longitudinal Data
• Functional-Coefficient Regression Models for Nonlinear Time Series
• Varying-coefficient models and basis function approximations for the analysis of repeated measurements
• A kernel method of estimating structured nonparametric regression based on marginal integration

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