Statistics and Its Interface
Volume 13 (2020)
Bi-level variable selection in high-dimensional Tobit models
Pages: 151 – 156
To study variable selection for high-dimensional Tobit models, we formulate Tobit models to single-index models. We hybrid group variable selection procedures for single index models and univariate regression methods for Tobit models to achieve variable selection for Tobit models with group structures taken into consideration. The procedure is computationally efficient and easily implemented. Finite sample experiments show its promising performance. We also illustrate its utility by analyzing a dataset from an HIV/AIDS study.
group structure, group lasso, single-index models, Tobit models
Hua Liang’s research was partially supported by NSF grant DMS-1620898.
Received 27 April 2019
Received revised 5 August 2019
Accepted 6 August 2019