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Local influence analysis in general spatial models

Author

Listed:
  • Xiaowen Dai

    (Renmin University of China)

  • Libin Jin

    (Renmin University of China)

  • Lei Shi

    (Yunnan University of Finance and Economics
    Yunnan TongChuang Scientific Computation and Data Mining Center)

  • Cuiping Yang

    (Yunnan University of Finance and Economics)

  • Shuangzhe Liu

    (University of Canberra)

Abstract

We study the local influence in the general spatial model which includes the spatial autoregressive model and the spatial error model as two special cases. The stepwise local influence procedure is employed in our diagnostic analysis. We derive the local diagnostic measures in the general spatial model under three perturbation schemes, namely, the variance perturbation, dependent variable perturbation and explanatory variable perturbation schemes. A simulation example and two real-data examples are analysed in detail and they show that the stepwise local influence analysis is effective in identifying influential observations and is a powerful tool for uncovering masking effects.

Suggested Citation

  • Xiaowen Dai & Libin Jin & Lei Shi & Cuiping Yang & Shuangzhe Liu, 2016. "Local influence analysis in general spatial models," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 100(3), pages 313-331, July.
    • Handle: RePEc:spr:alstar:v:100:y:2016:i:3:d:10.1007_s10182-015-0261-9
    DOI: 10.1007/s10182-015-0261-9
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    References listed on IDEAS

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    Cited by:

    1. Xiaowen Dai & Libin Jin & Maozai Tian & Lei Shi, 2019. "Bayesian Local Influence for Spatial Autoregressive Models with Heteroscedasticity," Statistical Papers, Springer, vol. 60(5), pages 1423-1446, October.

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