Quantile regression for dynamic partially linear varying coefficient time series models

DOI10.1016/J.JMVA.2015.06.013zbMath1327.62307OpenAlexW776991336MaRDI QIDQ746867

Heng Lian

Publication date: 21 October 2015

Published in: Journal of Multivariate Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jmva.2015.06.013

zbMATH Keywords

splinesautoregressive modelsSCAD penaltySchwarz information criterion (SIC)model structure recovery

Mathematics Subject Classification ID

Asymptotic properties of nonparametric inference (62G20)

Related Items (8)

Empirical likelihood in varying-coefficient quantile regression with missing observations ⋮ Residuals based Kolmogorov-Smirnov and Cramér-von Mises tests for varying coefficient models ⋮ Quantile regression of ultra-high dimensional partially linear varying-coefficient model with missing observations ⋮ Model averaging for semiparametric varying coefficient quantile regression models ⋮ Bayesian empirical likelihood of quantile regression with missing observations ⋮ Linear regression models with multiplicative distortions under new identifiability conditions ⋮ Penalized kernel quantile regression for varying coefficient models ⋮ Robust check loss-based inference of semiparametric models and its application in environmental data

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• The Adaptive Lasso and Its Oracle Properties
• Statistical methods with varying coefficient models
• Semiparametric quantile regression estimation in dynamic models with partially varying coefficients
• Conditional growth charts. (With discussion and rejoinder)
• One-step sparse estimates in nonconcave penalized likelihood models
• Variable selection in nonparametric additive models
• Quantile regression with varying coefficients
• Quantile regression in partially linear varying coefficient models
• Statistical estimation in varying coefficient models
• Nonlinear time series. Nonparametric and parametric methods
• Nonconcave penalized likelihood with a diverging number of parameters.
• Variable selection in semiparametric regression modeling
• Local Rank Inference for Varying Coefficient Models
• Convergence rate of b-spline estimators of nonparametric conditional quantile functions^∗
• NONPARAMETRIC ESTIMATION OF VARYING COEFFICIENT DYNAMIC PANEL DATA MODELS
• Local Linear Quantile Regression
• Regression Quantiles
• Nonparametric smoothing estimates of time-varying coefficient models with longitudinal data
• Smoothing Spline Estimation for Varying Coefficient Models With Repeatedly Measured Dependent Variables
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Smoothing Spline Models for the Analysis of Nested and Crossed Samples of Curves
• 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
• Sparsity and Smoothness Via the Fused Lasso
• Smoothing Spline Estimation in Varying-Coefficient Models
• Shrinkage Estimation of the Varying Coefficient Model
• Variable Selection in Nonparametric Varying-Coefficient Models for Analysis of Repeated Measurements
• Nonparametric Quantile Estimations for Dynamic Smooth Coefficient Models

This page was built for publication: Quantile regression for dynamic partially linear varying coefficient time series models