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Deep learning for higher-order nonparametric spatial autoregressive model

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Abstract

Deep learning technology has been successfully applied in more and more fields. In this paper, the application of deep neural networks in higher-order nonparametric spatial autoregressive models is studied. For spatial model, we propose the higher-order nonparametric spatial autoregressive neural network (HNSARNN) to fit the model. This method offers both good interpretability and prediction performance, and solves the black box problem in deep learning models to some degree. In various scenarios of spatial data distribution, the proposed method demonstrates superior performance compared to traditional approaches for handling nonparametric functions (such as the B-spline method). Simulation results show the effectiveness of the proposed model.

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Acknowledgements

The researches are supported by the National Key Research and Development Program of China (2021YFA1000102).

Funding

This study was supported by the National Key Research and Development Program of China (2021YFA1000102).

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Authors and Affiliations

  1. College of Science, China University of Petroleum, Qingdao, 266580, People’s Republic of China

    Zitong Li & Yunquan Song

  2. School of Economics and Management, China University of Petroleum, Qingdao, 266580, People’s Republic of China

    Ling Jian

Authors
  1. Zitong Li
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  2. Yunquan Song
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  3. Ling Jian
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Contributions

Prof. Yunquan Song: study conception and design, development of methodology; Zitong Li: data analysis, interpretation, and manuscript preparation and editing; Ling Jian: study conception and design.

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Correspondence to Yunquan Song.

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Li, Z., Song, Y. & Jian, L. Deep learning for higher-order nonparametric spatial autoregressive model. Appl Intell 54, 7570–7580 (2024). https://doi.org/10.1007/s10489-024-05541-8

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