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Variable Selection for Spatial Logistic Autoregressive Models

Author

Listed:
  • 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)

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.

Suggested Citation

  • Jiaxuan Liang & Yi Cheng & Yuqi Su & Shuyue Xiao & Yunquan Song, 2022. "Variable Selection for Spatial Logistic Autoregressive Models," Mathematics, MDPI, vol. 10(17), pages 1-16, August.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:17:p:3095-:d:900266
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    References listed on IDEAS

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    1. Song, Yunquan & Liang, Xijun & Zhu, Yanji & Lin, Lu, 2021. "Robust variable selection with exponential squared loss for the spatial autoregressive model," Computational Statistics & Data Analysis, Elsevier, vol. 155(C).
    2. Fan J. & Li R., 2001. "Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1348-1360, December.
    3. Han, Xiaoyi & Hsieh, Chih-Sheng & Lee, Lung-fei, 2017. "Estimation and model selection of higher-order spatial autoregressive model: An efficient Bayesian approach," Regional Science and Urban Economics, Elsevier, vol. 63(C), pages 97-120.
    4. Lung-Fei Lee, 2004. "Asymptotic Distributions of Quasi-Maximum Likelihood Estimators for Spatial Autoregressive Models," Econometrica, Econometric Society, vol. 72(6), pages 1899-1925, November.
    5. Liv Osland, 2010. "An Application of Spatial Econometrics in Relation to Hedonic House Price Modelling," Journal of Real Estate Research, American Real Estate Society, vol. 32(3), pages 289-320.
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    Cited by:

    1. Said Obakrim & Pierre Ailliot & Valérie Monbet & Nicolas Raillard, 2024. "EM algorithm for generalized Ridge regression with spatial covariates," Environmetrics, John Wiley & Sons, Ltd., vol. 35(6), September.

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