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Accommodation of outliers by robust MML estimation for spatial autoregressive model

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  • Sweta Shukla

    (Institute of Applied Sciences and Humanities, GLA University)

  • S. Lalitha

    (University of Allahabad)

  • Pulkit Srivastava

    (Faculty of Mathematical Sciences, University of Delhi)

Abstract

Some outliers might be undetected even after using the outlier detection procedures. In this paper, an accommodation procedure for such undetected outliers is discussed, which is done by robust modified maximum likelihood (MML) estimation of Type II censored sample for a Spatial Autoregressive (SAR) model. A new method is proposed for determining the number of observation to be censored to obtain robust parameter estimates. Next, a Monte-Carlo simulation study is carried to assess the robustness of the obtained MML estimators for both the normal and the contaminated normal models. Also, asymptotic variances, covariances, and distributional results are obtained for the MML estimators.

Suggested Citation

  • Sweta Shukla & S. Lalitha & Pulkit Srivastava, 2023. "Accommodation of outliers by robust MML estimation for spatial autoregressive model," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 14(1), pages 293-306, March.
    • Handle: RePEc:spr:ijsaem:v:14:y:2023:i:1:d:10.1007_s13198-023-01856-w
    DOI: 10.1007/s13198-023-01856-w
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    References listed on IDEAS

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    1. Schelin, Lina & Sjöstedt-de Luna, Sara, 2014. "Spatial prediction in the presence of left-censoring," Computational Statistics & Data Analysis, Elsevier, vol. 74(C), pages 125-141.
    2. Douglas M. Hawkins, 1980. "Critical Values for Identifying Outliers," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 29(1), pages 95-96, March.
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