Department of Statistics and Finance, School of Management, University of Science and Technology of China, Hefei, People's Republic of China
School of Artificial Intelligence and Big Data, Hefei University, Hefei, People's Republic of China
Department of Statistics and Finance, School of Management, University of Science and Technology of China, Hefei, People's Republic of China
zwp@ustc.edu.cn
This paper proposes a penalized method for high-dimensional variable selection and subgroup identification in the Tobit model. Based on Olsen's [(1978). Note on the uniqueness of the maximum likelihood estimator for the Tobit model. Econometrica: Journal of the Econometric Society, 46(5), 1211–1215. https://doi.org/10.2307/1911445] convex reparameterization of the Tobit negative log-likelihood, we develop an efficient algorithm for minimizing the objective function by combining the alternating direction method of multipliers (ADMM) and generalised coordinate descent (GCD). We also establish the oracle properties of our proposed estimator under some mild regularity conditions. Furthermore, extensive simulations and an empirical data study are conducted to demonstrate the performance of the proposed approach.
To cite this article: Yu Zhang, Jiangli Wang & Weiping Zhang (13 Mar 2024): Variable selection and subgroup analysis for high-dimensional censored data, Statistical Theory and Related Fields, DOI: 10.1080/24754269.2024.2327113
To link to this article: https://doi.org/10.1080/24754269.2024.2327113