Variable Selection for Spatial Logistic Autoregressive Models

When the spatial response variables are discrete, the spatial logistic autoregressive model adds an additional network structure to the ordinary logistic regression model to improve the classification accuracy. With the emergence of high-dimensional data in various fields, sparse spatial logistic regression models have attracted a great deal of interest from researchers. For the high-dimensional spatial logistic autoregressive model, in this paper, we propose a variable selection method with for the spatial logistic model. To identify important variables and make predictions, one efficient algorithm is employed to solve the penalized likelihood function. Simulations and a real example show that our methods perform well in a limited sample.

Automated summary

When the spatial response variables are discrete, the spatial logistic autoregressive model improves classification accuracy by adding an additional network structure to the ordinary logistic regression model. Sparse spatial logistic regression models have attracted significant attention due to the emergence of high-dimensional data in various fields. In this paper, a variable selection method is proposed for the high-dimensional spatial logistic autoregressive model. The penalized likelihood function is efficiently solved using an algorithm to identify important variables and make predictions. Simulations and a real example demonstrate the good performance of the proposed methods in a limited sample size.