Variable Selection for Spatial Logistic Autoregressive Models

Jiaxuan Liang, Yi Cheng, Yuqi Su, Shuyue Xiao and Yunquan Song ()
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Jiaxuan Liang: School of Science, China University of Petroleum, Qingdao 266580, China
Yi Cheng: School of Science, China University of Petroleum, Qingdao 266580, China
Yuqi Su: School of Science, China University of Petroleum, Qingdao 266580, China
Shuyue Xiao: School of Science, China University of Petroleum, Qingdao 266580, China
Yunquan Song: School of Science, China University of Petroleum, Qingdao 266580, China

Mathematics, 2022, vol. 10, issue 17, 1-16

Abstract: 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.

Keywords: spatial logistic autoregressive model; variable selection; maximum likelihood (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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