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Clustering dependent observations with copula functions

Author

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  • 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
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    References listed on IDEAS

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    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.
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    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).

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    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

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