Confidence intervals for high-dimensional partially linear single-index models

DOI10.1016/j.jmva.2016.03.007zbMath1341.62107OpenAlexW2314781732MaRDI QIDQ290693

Thomas Gueuning, Gerda Claeskens

Publication date: 3 June 2016

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

Full work available at URL: https://doi.org/10.1016/j.jmva.2016.03.007

zbMATH Keywords

asymptotic normalityconfidence intervalhigh-dimensional datasparsitysingle-index modelregularized estimation

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Nonparametric regression and quantile regression (62G08) Parametric tolerance and confidence regions (62F25) Ridge regression; shrinkage estimators (Lasso) (62J07)

Related Items

High-dimensional sufficient dimension reduction through principal projections, Single-index composite quantile regression for ultra-high-dimensional data, Variable selection and debiased estimation for single‐index expectile model, Uniformly valid inference for partially linear high-dimensional single-index models, Empirical likelihood in single-index quantile regression with high dimensional and missing observations, Model-Assisted Uniformly Honest Inference for Optimal Treatment Regimes in High Dimension, A High‐dimensional Focused Information Criterion, Empirical likelihood for partially linear single-index models under negatively associated errors

Uses Software

• flare
• covTest

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Estimation and testing for partially linear single-index models
• On asymptotically optimal confidence regions and tests for high-dimensional models
• The Adaptive Lasso and Its Oracle Properties
• On various confidence intervals post-model-selection
• Rates of convergence of the adaptive LASSO estimators to the oracle distribution and higher order refinements by the bootstrap
• Estimation for a partial-linear single-index model
• Variable selection in nonparametric additive models
• Semiparametric methods in econometrics
• Least angle regression. (With discussion)
• A dimension reduction based approach for estimation and variable selection in partially linear single-index models with high-dimensional covariates
• A significance test for the lasso
• Shrinkage estimation of partially linear single-index models
• Semi-parametric estimation of partially linear single-index models
• High-dimensional graphs and variable selection with the Lasso
• Confidence Intervals and Hypothesis Testing for High-Dimensional Regression
• Scaled sparse linear regression
• Empirical Likelihood Confidence Regions in a Partially Linear Single-Index Model
• Generalized Partially Linear Single-Index Models
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Penalized Spline Estimation for Partially Linear Single-Index Models
• Frequentist Model Average Estimators
• On extended partially linear single-index models
• Doubly Robust and Efficient Estimators for Heteroscedastic Partially Linear Single-Index Models Allowing high Dimensional Covariates
• Confidence Intervals for Low Dimensional Parameters in High Dimensional Linear Models