Estimation of semiparametric regression model with right-censored high-dimensional data

DOI10.1080/00949655.2019.1572757OpenAlexW2912103814MaRDI QIDQ5107372

Ersin Yilmaz, Dursun Aydın, S. Ejaz Ahmed

Publication date: 27 April 2020

Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00949655.2019.1572757

zbMATH Keywords

smoothing splinesemiparametric modelshigh-dimensional dataright-censored dataLASSOdouble-penalized least squares

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Ridge regression; shrinkage estimators (Lasso) (62J07) Censored data models (62N01)

Related Items (3)

Robust restricted Liu estimator in censored semiparametric linear models ⋮ Asymptotics of estimators for nonparametric multivariate regression models with long memory ⋮ Robust ridge estimator in censored semiparametric linear models

Uses Software

• SemiPar

Cites Work

• On asymptotically optimal confidence regions and tests for high-dimensional models
• Nearly unbiased variable selection under minimax concave penalty
• The Adaptive Lasso and Its Oracle Properties
• Automatic model selection for partially linear models
• Polynomial regression with censored data based on preliminary nonparametric estimation
• SCAD-penalized regression in high-dimensional partially linear models
• Regression analysis with randomly right-censored data
• Asymptotic normality of the `synthetic data' regression estimator for censored survival data
• Locally adaptive regression splines
• Semiparametric efficiency bounds for high-dimensional models
• Nonconcave penalized likelihood with a diverging number of parameters.
• Least angle regression. (With discussion)
• The central limit theorem under random censorship
• Variable selection in partly linear regression model with diverging dimensions for right censored data
• Nonparametric Estimation from Incomplete Observations
• Semiparametric Regression
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Asymptotic Statistics
• Post selection shrinkage estimation for high‐dimensional data analysis
• Sparsity and Smoothness Via the Fused Lasso
• Safe Feature Elimination in Sparse Supervised Learning
• Modified estimators in semiparametric regression models with right-censored data
• The Partial Linear Model in High Dimensions
• Regularization and Variable Selection Via the Elastic Net
• Censored partial regression
• Strong Rules for Discarding Predictors in Lasso-Type Problems
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item

This page was built for publication: Estimation of semiparametric regression model with right-censored high-dimensional data