IDEAS home Printed from https://ideas.repec.org/a/taf/japsta/v43y2016i3p509-525.html
   My bibliography  Save this article

Identification and classification of multiple outliers, high leverage points and influential observations in linear regression

Author

Listed:
  • A.A.M. Nurunnabi
  • M. Nasser
  • A.H.M.R. Imon

Abstract

Detection of multiple unusual observations such as outliers, high leverage points and influential observations (IOs) in regression is still a challenging task for statisticians due to the well-known masking and swamping effects. In this paper we introduce a robust influence distance that can identify multiple IOs, and propose a sixfold plotting technique based on the well-known group deletion approach to classify regular observations, outliers, high leverage points and IOs simultaneously in linear regression. Experiments through several well-referred data sets and simulation studies demonstrate that the proposed algorithm performs successfully in the presence of multiple unusual observations and can avoid masking and/or swamping effects.

Suggested Citation

  • A.A.M. Nurunnabi & M. Nasser & A.H.M.R. Imon, 2016. "Identification and classification of multiple outliers, high leverage points and influential observations in linear regression," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(3), pages 509-525, March.
    • Handle: RePEc:taf:japsta:v:43:y:2016:i:3:p:509-525
    DOI: 10.1080/02664763.2015.1070806
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/02664763.2015.1070806
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/02664763.2015.1070806?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. Menjoge, Rajiv S. & Welsch, Roy E., 2010. "A diagnostic method for simultaneous feature selection and outlier identification in linear regression," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 3181-3193, December.
    2. M. Habshah & M. R. Norazan & A.H.M. Rahmatullah Imon, 2009. "The performance of diagnostic-robust generalized potentials for the identification of multiple high leverage points in linear regression," Journal of Applied Statistics, Taylor & Francis Journals, vol. 36(5), pages 507-520.
    3. Billor, Nedret & Hadi, Ali S. & Velleman, Paul F., 2000. "BACON: blocked adaptive computationally efficient outlier nominators," Computational Statistics & Data Analysis, Elsevier, vol. 34(3), pages 279-298, September.
    4. Hadi, Ali S., 1992. "A new measure of overall potential influence in linear regression," Computational Statistics & Data Analysis, Elsevier, vol. 14(1), pages 1-27, June.
    5. A. H. M. Rahmatullah Imon, 2005. "Identifying multiple influential observations in linear regression," Journal of Applied Statistics, Taylor & Francis Journals, vol. 32(9), pages 929-946.
    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. Vilijandas Bagdonavičius & Linas Petkevičius, 2020. "A new multiple outliers identification method in linear regression," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 83(3), pages 275-296, April.
    2. A.A.M. Nurunnabi & Ali S. Hadi & A.H.M.R. Imon, 2014. "Procedures for the identification of multiple influential observations in linear regression," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(6), pages 1315-1331, June.
    3. M. Habshah & M. R. Norazan & A.H.M. Rahmatullah Imon, 2009. "The performance of diagnostic-robust generalized potentials for the identification of multiple high leverage points in linear regression," Journal of Applied Statistics, Taylor & Francis Journals, vol. 36(5), pages 507-520.
    4. Junlong Zhao & Chao Liu & Lu Niu & Chenlei Leng, 2019. "Multiple influential point detection in high dimensional regression spaces," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 81(2), pages 385-408, April.
    5. Kondylis, Athanassios & Hadi, Ali S., 2006. "Derived components regression using the BACON algorithm," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 556-569, November.
    6. Hock Ann Lim & Habshah Midi, 2016. "Diagnostic Robust Generalized Potential Based on Index Set Equality (DRGP (ISE)) for the identification of high leverage points in linear model," Computational Statistics, Springer, vol. 31(3), pages 859-877, September.
    7. Catherine Fuss & Angelos Theodorakopoulos, 2018. "Compositional Changes in Aggregate Productivity in an Era of Globalisation and Financial Crisis," Working Papers of VIVES - Research Centre for Regional Economics 627696, KU Leuven, Faculty of Economics and Business (FEB), VIVES - Research Centre for Regional Economics.
    8. L. Pitsoulis & G. Zioutas, 2010. "A fast algorithm for robust regression with penalised trimmed squares," Computational Statistics, Springer, vol. 25(4), pages 663-689, December.
    9. Cristian BARRA & Roberto ZOTTI, 2019. "Bank Performance, Financial Stability And Market Concentration: Evidence From Cooperative And Non‐Cooperative Banks," Annals of Public and Cooperative Economics, Wiley Blackwell, vol. 90(1), pages 103-139, March.
    10. Hong Choon Ong & Ekele Alih, 2015. "A Control Chart Based on Cluster-Regression Adjustment for Retrospective Monitoring of Individual Characteristics," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-30, April.
    11. Stefani, Gianluca & Gadanakis, Yiorgos & Lombardi, Ginevra Virginia & Tiberti, Marco, 2017. "The impact of financial leverage on farms capacity to react in market shocks," 2017 International Congress, August 28-September 1, 2017, Parma, Italy 261156, European Association of Agricultural Economists.
    12. Batalla-Bejerano, Joan & Costa-Campi, Maria Teresa & Trujillo-Baute, Elisa, 2016. "Collateral effects of liberalisation: Metering, losses, load profiles and cost settlement in Spain’s electricity system," Energy Policy, Elsevier, vol. 94(C), pages 421-431.
    13. Jiří Schwarz & Martin Pospíšil, 2018. "Bankruptcy, Investment, and Financial Constraints: Evidence from the Czech Republic," Eastern European Economics, Taylor & Francis Journals, vol. 56(2), pages 99-121, March.
    14. A. H. M. Rahmatullah Imon, 2005. "Identifying multiple influential observations in linear regression," Journal of Applied Statistics, Taylor & Francis Journals, vol. 32(9), pages 929-946.
    15. AMENDOLA, Adalgiso & BARRA, Cristian & BOCCIA, Marinella & PAPACCIO, Anna, 2018. "Market Structure and Financial Stability: Theory and Evidence," CELPE Discussion Papers 156, CELPE - CEnter for Labor and Political Economics, University of Salerno, Italy.
    16. Julia Cage, 2015. "Measuring Policy Performance: Can We Do Better than the World Bank?," Post-Print hal-03392964, HAL.
    17. Fratianni, Michele & Marchionne, Francesco, 2017. "Bank asset reallocation and sovereign debt," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 47(C), pages 15-32.
    18. Sylvain Weber, 2010. "bacon: An effective way to detect outliers in multivariate data using Stata (and Mata)," Stata Journal, StataCorp LP, vol. 10(3), pages 331-338, September.
    19. Mele, Antonio & Paglialunga, Elena & Sforna, Giorgia, 2021. "Climate cooperation from Kyoto to Paris: What can be learnt from the CDM experience?," Socio-Economic Planning Sciences, Elsevier, vol. 75(C).
    20. Sevvandi Kandanaarachchi & Mario A Munoz & Rob J Hyndman & Kate Smith-Miles, 2018. "On normalization and algorithm selection for unsupervised outlier detection," Monash Econometrics and Business Statistics Working Papers 16/18, Monash University, Department of Econometrics and Business Statistics.

    More about this item

    Statistics

    Access and download statistics

    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:taf:japsta:v:43:y:2016:i:3:p:509-525. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/CJAS20 .

    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.