Statistics and Its Interface

Volume 13 (2020)

Number 2

Bi-level variable selection in high-dimensional Tobit models

Pages: 151 – 156

DOI: https://dx.doi.org/10.4310/SII.2020.v13.n2.a1

Authors

Hailin Huang (Department of Statistics, George Washington University, Washington, D.C., U.S.A.)

Jizi Shangguan (Department of Statistics, George Washington University, Washington, D.C., U.S.A.)

Yuanzhang Li (Department of Statistics, George Washington University, Washington, D.C., U.S.A.)

Hua Liang (Department of Statistics, George Washington University, Washington, D.C., U.S.A.)

Abstract

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.

Keywords

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