Variable Selection in Semiparametric Quantile Modeling for Longitudinal Data

DOI10.1080/03610926.2013.857418zbMath1329.62178OpenAlexW2084993005MaRDI QIDQ3462363

Lu Lin, Kang-Ning Wang

Publication date: 5 January 2016

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

Full work available at URL: https://doi.org/10.1080/03610926.2013.857418

zbMATH Keywords

longitudinal dataquantile regressionvariable selectionoracle property

Mathematics Subject Classification ID

Asymptotic distribution theory in statistics (62E20) Nonparametric estimation (62G05) General nonlinear regression (62J02)

Related Items

Spatial local linear estimation of the L₁-conditional quantiles for functional regressors ⋮ Gaussian copula based composite quantile regression in semivarying models with longitudinal data ⋮ Modal regression statistical inference for longitudinal data semivarying coefficient models: generalized estimating equations, empirical likelihood and variable selection ⋮ Copula and composite quantile regression-based estimating equations for longitudinal data ⋮ Functional data analysis: local linear estimation of the \(L_1\)-conditional quantiles ⋮ Efficient parameter estimation and variable selection in partial linear varying coefficient quantile regression model with longitudinal data

Cites Work

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• New efficient estimation and variable selection methods for semiparametric varying-coefficient partially linear models
• Efficient estimation for semivarying-coefficient models
• Convergence rate of b-spline estimators of nonparametric conditional quantile functions^∗
• Estimation in a semiparametric model for longitudinal data with unspecified dependence structure
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• Shrinkage Estimation of the Varying Coefficient Model
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• Quadratic Inference Functions for Varying‐Coefficient Models with Longitudinal Data
• New Estimation and Model Selection Procedures for Semiparametric Modeling in Longitudinal Data Analysis

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