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Outlier detection and accommodation in general spatial models

Author

Listed:
  • Xiaowen Dai

    (Renmin University of China)

  • Libin Jin

    (Renmin University of China)

  • Anqi Shi

    (University of Wisconsin-Madison)

  • Lei Shi

    (Yunnan University of Finance and Economics)

Abstract

This paper studies outlier detection and accommodation in general spatial models including spatial autoregressive models and spatial error model as special cases. Using mean-shift and variance-weight models respectively, test statistics for multiple outliers are derived and the detecting procedures are proposed. In addition, several key diagnostic measures such as standardized residuals and leverage measure are defined in general spatial models. Outlier modified models are proposed to accommodate outliers in the data set. The performance of test statistics, including size and power, are examined via simulation studies. Three real examples are analyzed and the results show that the proposed methodology is useful for identifying and accommodating outliers in general spatial models.

Suggested Citation

  • Xiaowen Dai & Libin Jin & Anqi Shi & Lei Shi, 2016. "Outlier detection and accommodation in general spatial models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 25(3), pages 453-475, August.
    • Handle: RePEc:spr:stmapp:v:25:y:2016:i:3:d:10.1007_s10260-015-0348-1
    DOI: 10.1007/s10260-015-0348-1
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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.
    2. Tianfa Xie & Ruiyuan Cao & Jiang Du, 2020. "Variable selection for spatial autoregressive models with a diverging number of parameters," Statistical Papers, Springer, vol. 61(3), pages 1125-1145, June.
    3. Ali Mohammed Baba & Habshah Midi & Nur Haizum Abd Rahman, 2022. "Spatial Outlier Accommodation Using a Spatial Variance Shift Outlier Model," Mathematics, MDPI, vol. 10(17), pages 1-19, September.
    4. Xiaowen Dai & Shidan Huang & Libin Jin & Maozai Tian, 2023. "Wild Bootstrap-Based Bias Correction for Spatial Quantile Panel Data Models with Varying Coefficients," Mathematics, MDPI, vol. 11(9), pages 1-16, April.

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