Variable Selection for Nonparametric Tobit Model Via Deep Learning

Tobit models are widely used to explore the relationship between response variables and covariates when the response variable is censored. Since the traditional Tobit model cannot capture complex nonlinear relationships, it may not be applicable to the analysis of many datasets. To overcome this problem, we replace the linear component of the Tobit model with a nonparametric component, thus expanding the traditional Tobit model into a new nonparametric model. Combined with the advantages of deep neural networks in capturing the feature space of complex structures, we propose a likelihood-based variable selection method for Tobit models that combines variable selection with deep learning. To evaluate the effectiveness of the method, we conducted simulation experiments and example data experiments to demonstrate the good results of the method.