IDEAS home Printed from https://ideas.repec.org/a/spr/ijsaem/v14y2023i1d10.1007_s13198-023-01856-w.html
   My bibliography  Save this article

Accommodation of outliers by robust MML estimation for spatial autoregressive model

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13198-023-01856-w
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s13198-023-01856-w?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Karol Pilot & Alicja Ganczarek-Gamrot & Krzysztof Kania, 2024. "Dealing with Anomalies in Day-Ahead Market Prediction Using Machine Learning Hybrid Model," Energies, MDPI, vol. 17(17), pages 1-20, September.
    2. Francesca Ieva & Anna Maria Paganoni, 2020. "Component-wise outlier detection methods for robustifying multivariate functional samples," Statistical Papers, Springer, vol. 61(2), pages 595-614, April.
    3. Andrzej Chmielowiec, 2021. "Algorithm for error-free determination of the variance of all contiguous subsequences and fixed-length contiguous subsequences for a sequence of industrial measurement data," Computational Statistics, Springer, vol. 36(4), pages 2813-2840, December.
    4. Marc Chataigner & Stéphane Crépey & Jiang Pu, 2020. "Nowcasting Networks," Post-Print hal-03910123, HAL.
    5. Greco, Salvatore & Ishizaka, Alessio & Tasiou, Menelaos & Torrisi, Gianpiero, 2019. "Sigma-Mu efficiency analysis: A methodology for evaluating units through composite indicators," European Journal of Operational Research, Elsevier, vol. 278(3), pages 942-960.
    6. David Juárez-Varón & Victoria Tur-Viñes & Alejandro Rabasa-Dolado & Kristina Polotskaya, 2020. "An Adaptive Machine Learning Methodology Applied to Neuromarketing Analysis: Prediction of Consumer Behaviour Regarding the Key Elements of the Packaging Design of an Educational Toy," Social Sciences, MDPI, vol. 9(9), pages 1-23, September.
    7. Zhongqiu Wang & Guan Yuan & Haoran Pei & Yanmei Zhang & Xiao Liu, 2020. "Unsupervised learning trajectory anomaly detection algorithm based on deep representation," International Journal of Distributed Sensor Networks, , vol. 16(12), pages 15501477209, December.
    8. Arata, Linda & Fabrizi, Enrico & Sckokai, Paolo, 2020. "A worldwide analysis of trend in crop yields and yield variability: Evidence from FAO data," Economic Modelling, Elsevier, vol. 90(C), pages 190-208.
    9. Wentao Yang & Huaxi He & Dongsheng Wei & Hao Chen, 2022. "Generating pseudo-absence samples of invasive species based on outlier detection in the geographical characteristic space," Journal of Geographical Systems, Springer, vol. 24(2), pages 261-279, April.
    10. Fournier, Nicholas PhD & Farid, Yashar Zeinali PhD & Patire, Anthony David PhD, 2021. "Potential Erroneous Degradation of High Occupancy Vehicle (HOV) Facilities," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt3z76r7tj, Institute of Transportation Studies, UC Berkeley.
    11. Puteri Paramita & Zuduo Zheng & Md Mazharul Haque & Simon Washington & Paul Hyland, 2018. "User satisfaction with train fares: A comparative analysis in five Australian cities," PLOS ONE, Public Library of Science, vol. 13(6), pages 1-26, June.
    12. Liqun Diao & Grace Y. Yi, 2023. "Classification Trees with Mismeasured Responses," Journal of Classification, Springer;The Classification Society, vol. 40(1), pages 168-191, April.
    13. Gasser, Patrick, 2020. "A review on energy security indices to compare country performances," Energy Policy, Elsevier, vol. 139(C).
    14. Nirpeksh Kumar, 2019. "Exact distributions of tests of outliers for exponential samples," Statistical Papers, Springer, vol. 60(6), pages 2031-2061, December.
    15. Stanley Munamato Mbiva & Fabio Mathias Correa, 2024. "Machine Learning to Enhance the Detection of Terrorist Financing and Suspicious Transactions in Migrant Remittances," JRFM, MDPI, vol. 17(5), pages 1-19, April.
    16. Taha Yehia & Ali Wahba & Sondos Mostafa & Omar Mahmoud, 2022. "Suitability of Different Machine Learning Outlier Detection Algorithms to Improve Shale Gas Production Data for Effective Decline Curve Analysis," Energies, MDPI, vol. 15(23), pages 1-25, November.
    17. Beata Gavurova & Jaroslav Belas & Katarina Zvarikova & Martin Rigelsky & Viera Ivankova, 2021. "The Effect of Education and R&D on Tourism Spending in OECD Countries: An Empirical Study," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 23(58), pages 806-806, August.
    18. Antenangeli Leonardo & Cantú Francisco, 2019. "Right on Time: An Electoral Audit for the Publication of Vote Results," Statistics, Politics and Policy, De Gruyter, vol. 10(2), pages 137-186, December.
    19. Yeon-Jin Sim & Jeongmin Kim & Jaehyeon Choi & Jun-Ho Huh, 2022. "System Design for Detecting Real Estate Speculation Abusing Inside Information: For the Fair Reallocation of Land," Land, MDPI, vol. 11(4), pages 1-17, April.
    20. Marcel Clermont & Julia Schaefer, 2019. "Identification of Outliers in Data Envelopment Analysis," Schmalenbach Business Review, Springer;Schmalenbach-Gesellschaft, vol. 71(4), pages 475-496, October.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:ijsaem:v:14:y:2023:i:1:d:10.1007_s13198-023-01856-w. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.