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Sparse Boosting for Additive Spatial Autoregressive Model with High Dimensionality

Author

Listed:
  • Mu Yue

    (School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore 639798, Singapore)

  • Jingxin Xi

    (School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore 639798, Singapore
    School of Ecology & Environment, Renmin University of China, Beijing 100872, China)

Abstract

Variable selection methods have been a focus in the context of econometrics and statistics literature. In this paper, we consider additive spatial autoregressive model with high-dimensional covariates. Instead of adopting the traditional regularization approaches, we offer a novel multi-step sparse boosting algorithm to conduct model-based prediction and variable selection. One main advantage of this new method is that we do not need to perform the time-consuming selection of tuning parameters. Extensive numerical examples illustrate the advantage of the proposed methodology. An application of Boston housing price data is further provided to demonstrate the proposed methodology.

Suggested Citation

  • Mu Yue & Jingxin Xi, 2025. "Sparse Boosting for Additive Spatial Autoregressive Model with High Dimensionality," Mathematics, MDPI, vol. 13(5), pages 1-16, February.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:5:p:757-:d:1599548
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