Penalized profile least squares-based statistical inference for varying coefficient partially linear errors-in-variables models

DOI10.1007/s11425-016-9108-yzbMath1404.62037OpenAlexW2796541478WikidataQ129984713 ScholiaQ129984713MaRDI QIDQ1989897

Guo-Liang Fan, Han-Ying Liang, Li Xing Zhu

Publication date: 29 October 2018

Published in: Science China. Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11425-016-9108-y

zbMATH Keywords

variable selectionpenalized likelihoodSCADvarying coefficient partially linear modeldiverging number of parameters

Mathematics Subject Classification ID

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

Related Items (10)

Empirical likelihood and variable selection for partially linear single-index EV models with missing censoring indicators ⋮ Weighted bias-corrected restricted statistical inference for heteroscedastic semiparametric varying-coefficient errors-in-variables model ⋮ Statistical inference for varying-coefficient partially linear errors-in-variables models with missing data ⋮ Quantile regression of ultra-high dimensional partially linear varying-coefficient model with missing observations ⋮ Robust empirical likelihood inference for partially linear varying coefficient models with longitudinal data ⋮ Functional regression with dependent error and missing observation in reproducing kernel Hilbert spaces ⋮ Estimation in partially linear varying-coefficient errors-in-variables models with missing response variables ⋮ Statistical inference for the heteroscedastic partially linear varying-coefficient errors-in-variables model with missing censoring indicators ⋮ Averaged and integrated estimations of varying-coefficient regression models with dependent observations ⋮ Jackknifing for partially linear varying-coefficient errors-in-variables model with missing response at random

Cites Work

• Unnamed Item
• Unnamed Item
• Penalized empirical likelihood for high-dimensional partially linear varying coefficient model with measurement errors
• Polynomial spline estimation for generalized varying coefficient partially linear models with a diverging number of components
• SCAD penalized rank regression with a diverging number of parameters
• Stable direction recovery in single-index models with a diverging number of predictors
• Local polynomial fitting in semivarying coefficient model
• Variable selection for varying-coefficient models with the sparse regularization
• Empirical likelihood for a varying coefficient partially linear model with diverging number of parameters
• Profile likelihood inferences on semiparametric varying-coefficient partially linear models
• One-step sparse estimates in nonconcave penalized likelihood models
• Estimation in a semiparametric partially linear errors-in-variables model
• Empirical likelihood for semivarying coefficient model with measurement error in the nonparametric part
• Nonconcave penalized likelihood with a diverging number of parameters.
• Variable selection and estimation for semi-parametric multiple-index models
• Spline estimation and variable selection for single-index prediction models with diverging number of index parameters
• On the adaptive elastic net with a diverging number of parameters
• Asymptotic properties of bridge estimators in sparse high-dimensional regression models
• New efficient estimation and variable selection methods for semiparametric varying-coefficient partially linear models
• Bias-corrected statistical inference for partially linear varying coefficient errors-in-variables models with restricted condition
• Efficient estimation of partially linear varying coefficient models
• The Dantzig selector: statistical estimation when \(p\) is much larger than \(n\). (With discussions and rejoinder).
• Variable selection in semiparametric regression modeling
• Empirical likelihood for semiparametric varying-coefficient partially linear regression models
• On Distribution-Weighted Partial Least Squares with Diverging Number of Highly Correlated Predictors
• Asymptotic properties of sufficient dimension reduction with a diverging number of predictors
• Asymptotic Statistics
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Tuning parameter selectors for the smoothly clipped absolute deviation method

This page was built for publication: Penalized profile least squares-based statistical inference for varying coefficient partially linear errors-in-variables models