IDEAS home Printed from https://ideas.repec.org/a/tsj/stataj/v10y2010i3p331-338.html
   My bibliography  Save this article

bacon: An effective way to detect outliers in multivariate data using Stata (and Mata)

Author

Listed:
  • Sylvain Weber

    (University of Geneva)

Abstract

Identifying outliers in multivariate data is computationally intensive. The bacon command, presented in this article, allows one to quickly identify out- liers, even on large datasets of tens of thousands of observations. bacon constitutes an attractive alternative to hadimvo, the only other command available in Stata for the detection of outliers. Copyright 2010 by StataCorp LP.

Suggested Citation

  • 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.
    • Handle: RePEc:tsj:stataj:v:10:y:2010:i:3:p:331-338
    Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj10-3/st0197/
    as

    Download full text from publisher

    File URL: http://www.stata-journal.com/article.html?article=st0197
    File Function: link to article download
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kit Baum, 2008. "Using Mata to work more effectively with Stata: A tutorial," Fall North American Stata Users' Group Meetings 2008 7, Stata Users Group.
    2. 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.
    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. 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.
    2. 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.
    3. 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.
    4. Peter Nemec, 2024. "Contesting the public works domain: examining the factors affecting presence and success of SMES in public procurement," Empirical Economics, Springer, vol. 67(5), pages 2135-2173, November.
    5. 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.
    6. 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.
    7. 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.
    8. José Ignacio Giménez-Nadal & José Alberto Molina & Jorge Velilla, 2024. "Intermediate activities while commuting," Review of Economics of the Household, Springer, vol. 22(3), pages 1185-1220, September.
    9. 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.
    10. Balcazar Salazar,Carlos Felipe, 2015. "Long-run effects of democracy on income inequality : evidence from repeated cross-sections," Policy Research Working Paper Series 7153, The World Bank.
    11. Claude Diebolt & Audrey-Rose Menard & Faustine Perrin, 2017. "Behind the fertility–education nexus: what triggered the French development process?," European Review of Economic History, European Historical Economics Society, vol. 21(4), pages 357-392.
    12. Basher, Syed Abul & Raboy, David G. & Kaitibie, Simeon & Hossain, Ishrat, 2012. "The economics of food security in Arab micro states: preliminary evidence from micro data," MPRA Paper 39357, University Library of Munich, Germany.
    13. M. Hubert & P. Rousseeuw & K. Vakili, 2014. "Shape bias of robust covariance estimators: an empirical study," Statistical Papers, Springer, vol. 55(1), pages 15-28, February.
    14. Oyvat, Cem, 2016. "Agrarian Structures, Urbanization, and Inequality," World Development, Elsevier, vol. 83(C), pages 207-230.
    15. Barra, Cristian & Zotti, Roberto, 2017. "On the relationship between bank market concentration and stability of financial institutions: Evidence from the Italian banking sector," MPRA Paper 79900, University Library of Munich, Germany.
    16. Sklenicka, Petr & Zouhar, Jan & Molnarova, Kristina Janeckova & Vlasak, Josef & Kottova, Blanka & Petrzelka, Peggy & Gebhart, Michal & Walmsley, Alena, 2020. "Trends of soil degradation: Does the socio-economic status of land owners and land users matter?," Land Use Policy, Elsevier, vol. 95(C).
    17. Cockx, Lara & Francken, Nathalie, 2014. "Extending the concept of the resource curse: Natural resources and public spending on health," Ecological Economics, Elsevier, vol. 108(C), pages 136-149.
    18. Salem Reyen & John Miller & Edward Wegman, 2009. "Separating a mixture of two normals with proportional covariances," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 70(3), pages 297-314, November.
    19. Ching‐Hua Yeh & Stefan Hirsch, 2023. "A meta‐regression analysis on the willingness‐to‐pay for country‐of‐origin labelling," Journal of Agricultural Economics, Wiley Blackwell, vol. 74(3), pages 719-743, September.
    20. 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.

    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:tsj:stataj:v:10:y:2010:i:3:p:331-338. 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: Christopher F. Baum or Lisa Gilmore (email available below). General contact details of provider: http://www.stata-journal.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.