High-Dimensional Censored Regression via the Penalized Tobit Likelihood

DOI10.1080/07350015.2023.2182309arXiv2203.02601MaRDI QIDQ6150365

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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

zbMATH Keywords

censored regressionTobit modelhigh dimensionscoordinate descentfolded concave penaltystrong oracle property

Mathematics Subject Classification ID

Applications of statistics to economics (62P20)

Cites Work

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• 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

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