Estimation and Identification of a Varying-Coefficient Additive Model for Locally Stationary Processes

DOI10.1080/01621459.2018.1482753zbMath1428.62394OpenAlexW2807474751MaRDI QIDQ5242468

Jin-hong You, Lixia Hu, Tao Huang

Publication date: 12 November 2019

Published in: Journal of the American Statistical Association (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/01621459.2018.1482753

zbMATH Keywords

B-splinelocally stationary processSCAD penalty functionvarying-coefficient additive model

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Asymptotic properties of nonparametric inference (62G20) Nonparametric estimation (62G05) Stationary stochastic processes (60G10)

Related Items

Unified statistical inference for a nonlinear dynamic functional/longitudinal data model, Estimation and Inference for Dynamic Single-Index Varying-Coefficient Models, Statistical inference of locally stationary functional coefficient models, Generalized varying-coefficient additive model for locally stationary time series, Unnamed Item, Boosting high dimensional predictive regressions with time varying parameters, New efficient spline estimation for varying-coefficient models with two-step knot number selection, A new approach to varying-coefficient additive models with longitudinal covariates

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• The Adaptive Lasso and Its Oracle Properties
• Nonparametric regression for locally stationary time series
• Statistical estimation in varying coefficient models
• Jackknife, bootstrap and other resampling methods in regression analysis
• Nonlinear time series. Nonparametric and parametric methods
• Time-varying nonlinear regression models: nonparametric estimation and model selection
• Estimating Optimal Transformations for Multiple Regression and Correlation
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Functional-Coefficient Regression Models for Nonlinear Time Series
• Varying-coefficient models and basis function approximations for the analysis of repeated measurements
• Varying-coefficient additive models for functional data