High-Dimensional Censored Regression via the Penalized Tobit Likelihood
From MaRDI portal
Publication:6150365
DOI10.1080/07350015.2023.2182309arXiv2203.02601MaRDI QIDQ6150365
No author found.
Publication date: 6 March 2024
Published in: Journal of Business & Economic Statistics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2203.02601
censored regressionTobit modelhigh dimensionscoordinate descentfolded concave penaltystrong oracle property
Cites Work
- Unnamed Item
- Unnamed Item
- Nearly unbiased variable selection under minimax concave penalty
- A unified approach to model selection and sparse recovery using regularized least squares
- Estimation of Relationships for Limited Dependent Variables
- The Adaptive Lasso and Its Oracle Properties
- Censored linear model in high dimensions. Penalised linear regression on high-dimensional data with left-censored response variable
- Tobit models: A survey
- One-step sparse estimates in nonconcave penalized likelihood models
- Least absolute deviations estimation for the censored regression model
- On Lasso for censored data
- Linear hypothesis testing for high dimensional generalized linear models
- Simultaneous analysis of Lasso and Dantzig selector
- Strong oracle optimality of folded concave penalized estimation
- Linear regression with censored data
- Note on the Uniqueness of the Maximum Likelihood Estimator for the Tobit Model
- Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
- Nonconcave Penalized Likelihood With NP-Dimensionality
- The dantzig selector for censored linear regression models
- LAD-Lasso variable selection for doubly censored median regression models
- Tuning Parameter Selection in High Dimensional Penalized Likelihood
This page was built for publication: High-Dimensional Censored Regression via the Penalized Tobit Likelihood
