IDEAS home Printed from https://ideas.repec.org/a/spr/stpapr/v60y2019i1d10.1007_s00362-016-0822-3.html
   My bibliography  Save this article

Clustering dependent observations with copula functions

Author

Listed:
  • F. Marta L. Lascio

    (University of Bozen-Bolzano)

  • Simone Giannerini

    (University of Bologna)

Abstract

This paper deals with the problem of clustering dependent observations according to their underlying complex generating process. Di Lascio and Giannerini (Journal of Classification 29(1):50–75, 2012) introduced the CoClust, a clustering algorithm based on copula function that achieves the task but has a high computational burden. Moreover, the CoClust automatically allocates all the observations to the clusters; thus, it cannot discard potentially irrelevant observations. In this paper we introduce an improved version of the CoClust that both overcomes these issues and performs better in many respects. By means of a Monte Carlo study we investigate the features of the algorithm and show that it improves consistently with respect to the old CoClust. The validity of our proposal is also supported by applications to real data sets of human breast tumor samples for which the algorithm provides a meaningful biological interpretation. The new algorithm is implemented and made available through an updated version of the R package CoClust.

Suggested Citation

  • F. Marta L. Lascio & Simone Giannerini, 2019. "Clustering dependent observations with copula functions," Statistical Papers, Springer, vol. 60(1), pages 35-51, February.
    • Handle: RePEc:spr:stpapr:v:60:y:2019:i:1:d:10.1007_s00362-016-0822-3
    DOI: 10.1007/s00362-016-0822-3
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s00362-016-0822-3
    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/s00362-016-0822-3?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 look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    1. F. Lascio & Simone Giannerini, 2012. "A Copula-Based Algorithm for Discovering Patterns of Dependent Observations," Journal of Classification, Springer;The Classification Society, vol. 29(1), pages 50-75, April.
    2. Alberto Roverato & F. Marta L. Di Lascio, 2011. "Wilks' Λ Dissimilarity Measures for Gene Clustering: An Approach Based on the Identification of Transcription Modules," Biometrics, The International Biometric Society, vol. 67(4), pages 1236-1248, December.
    3. Brechmann, Eike Christian & Schepsmeier, Ulf, 2013. "Modeling Dependence with C- and D-Vine Copulas: The R Package CDVine," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 52(i03).
    4. Clarke, Kevin A., 2007. "A Simple Distribution-Free Test for Nonnested Model Selection," Political Analysis, Cambridge University Press, vol. 15(3), pages 347-363, July.
    5. F. Di Lascio & Simone Giannerini & Alessandra Reale, 2015. "Exploring copulas for the imputation of complex dependent data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 24(1), pages 159-175, March.
    6. Zimmer, David M. & Trivedi, Pravin K., 2006. "Using Trivariate Copulas to Model Sample Selection and Treatment Effects: Application to Family Health Care Demand," Journal of Business & Economic Statistics, American Statistical Association, vol. 24, pages 63-76, January.
    7. Vuong, Quang H, 1989. "Likelihood Ratio Tests for Model Selection and Non-nested Hypotheses," Econometrica, Econometric Society, vol. 57(2), pages 307-333, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Fuchs, Sebastian & Di Lascio, F. Marta L. & Durante, Fabrizio, 2021. "Dissimilarity functions for rank-invariant hierarchical clustering of continuous variables," Computational Statistics & Data Analysis, Elsevier, vol. 159(C).

    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 Wyszynski & Giampiero Marra, 2018. "Sample selection models for count data in R," Computational Statistics, Springer, vol. 33(3), pages 1385-1412, September.
    2. Koliai, Lyes, 2016. "Extreme risk modeling: An EVT–pair-copulas approach for financial stress tests," Journal of Banking & Finance, Elsevier, vol. 70(C), pages 1-22.
    3. Dalla Valle, Luciana & De Giuli, Maria Elena & Tarantola, Claudia & Manelli, Claudio, 2016. "Default probability estimation via pair copula constructions," European Journal of Operational Research, Elsevier, vol. 249(1), pages 298-311.
    4. Eling, Martin & Jung, Kwangmin, 2018. "Copula approaches for modeling cross-sectional dependence of data breach losses," Insurance: Mathematics and Economics, Elsevier, vol. 82(C), pages 167-180.
    5. Mikhail Semenov & Daulet Smagulov, 2017. "Portfolio Risk Assessment using Copula Models," Papers 1707.03516, arXiv.org.
    6. Seok, Sang Ik & Cho, Hoon & Ryu, Doojin, 2020. "The information content of funds from operations and net income in real estate investment trusts," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).
    7. Zhang, Dalu, 2014. "Vine copulas and applications to the European Union sovereign debt analysis," International Review of Financial Analysis, Elsevier, vol. 36(C), pages 46-56.
    8. Jörg Schwiebert, 2016. "Multinomial choice models based on Archimedean copulas," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 100(3), pages 333-354, July.
    9. Genius, Margarita & Stefanou, Spiro E. & Tzouvelekas, Vangelis, 2012. "Measuring productivity growth under factor non-substitution: An application to US steam-electric power generation utilities," European Journal of Operational Research, Elsevier, vol. 220(3), pages 844-852.
    10. Romina Gambacorta & Maria Iannario, 2013. "Measuring Job Satisfaction with CUB Models," LABOUR, CEIS, vol. 27(2), pages 198-224, June.
    11. Martin Magris, 2019. "A Vine-copula extension for the HAR model," Papers 1907.08522, arXiv.org.
    12. Fontaine, Charles & Frostig, Ron D. & Ombao, Hernando, 2020. "Modeling non-linear spectral domain dependence using copulas with applications to rat local field potentials," Econometrics and Statistics, Elsevier, vol. 15(C), pages 85-103.
    13. David Danz & Dietmar Fehr & Dorothea Kübler, 2012. "Information and beliefs in a repeated normal-form game," Experimental Economics, Springer;Economic Science Association, vol. 15(4), pages 622-640, December.
    14. Ait Sidhoum, Amer & Serra, Teresa, 2017. "Corporate social responsibility and dimensions of performance: An application to U.S. electric utilities," Utilities Policy, Elsevier, vol. 48(C), pages 1-11.
    15. Mildenberger, Carl David & Pietri, Antoine, 2018. "How does size matter for military success? Evidence from virtual worlds," Journal of Economic Behavior & Organization, Elsevier, vol. 154(C), pages 137-155.
    16. Stavrakoudis, Athanassios & Panagiotou, Dimitrios, 2016. "Price dependence and asymmetric responses between coffee varieties," Agricultural Economics Review, Greek Association of Agricultural Economists, vol. 17(2), June.
    17. Paul De Boer & Richard Paap, 2009. "Testing non‐nested demand relations: linear expenditure system versus indirect addilog," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 63(3), pages 368-384, August.
    18. Olesia Verchenko, 2011. "Testing option pricing models: complete and incomplete markets," Discussion Papers 38, Kyiv School of Economics.
    19. Benos, Nikos & Stavrakoudis, Athanassios, 2022. "Okun's law: Copula-based evidence from G7 countries," The Quarterly Review of Economics and Finance, Elsevier, vol. 84(C), pages 478-491.
    20. Czajkowski, Mikolaj & Buszko-Briggs, Malgorzata & Hanley, Nick, 2009. "Valuing changes in forest biodiversity," Ecological Economics, Elsevier, vol. 68(12), pages 2910-2917, October.

    More about this item

    Keywords

    Copula function; Multivariate dependence structure; Clustering; Biological tumor sample;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis

    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:spr:stpapr:v:60:y:2019:i:1:d:10.1007_s00362-016-0822-3. 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.