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Detection of outliers in mixed regressive-spatial autoregressive models

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  • Libin Jin
  • Xiaowen Dai
  • Anqi Shi
  • Lei Shi

Abstract

This article studies the outlier detection problem in mixed regressive-spatial autoregressive model. The formulae for testing outliers and their approximate distributions are derived under the mean-shift model and the variance-weight model, respectively. The simulation studies are conducted for examining the power and size of the test, as well as for the detection of outliers when a simulated data contains several outliers. A real data is analyzed to illustrate the proposed method, and modified models based on mean-shift and variance-weight models in which detected outliers are taken into account are suggested to deal with the outliers and confirm theconclusions.

Suggested Citation

  • Libin Jin & Xiaowen Dai & Anqi Shi & Lei Shi, 2016. "Detection of outliers in mixed regressive-spatial autoregressive models," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 45(17), pages 5179-5192, September.
    • Handle: RePEc:taf:lstaxx:v:45:y:2016:i:17:p:5179-5192
    DOI: 10.1080/03610926.2014.941493
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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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