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Agglomerative hierarchical clustering of continuous variables based on mutual information

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  • Kojadinovic, Ivan

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  • Kojadinovic, Ivan, 2004. "Agglomerative hierarchical clustering of continuous variables based on mutual information," Computational Statistics & Data Analysis, Elsevier, vol. 46(2), pages 269-294, June.
    • Handle: RePEc:eee:csdana:v:46:y:2004:i:2:p:269-294
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    References listed on IDEAS

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    1. Peter Hall & Sally Morton, 1993. "On the estimation of entropy," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 45(1), pages 69-88, March.
    2. Ellis, David M. & Flannery, Mark J., 1992. "Does the debt market assess large banks, risk? : Time series evidence from money center CDs," Journal of Monetary Economics, Elsevier, vol. 30(3), pages 481-502, December.
    3. Harry Joe, 1989. "Estimation of entropy and other functionals of a multivariate density," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 41(4), pages 683-697, December.
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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).
    2. JinXing Che & YouLong Yang, 2017. "Stochastic correlation coefficient ensembles for variable selection," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(10), pages 1721-1742, July.
    3. Wu, Edmond H.C. & Yu, Philip L.H. & Li, W.K., 2009. "A smoothed bootstrap test for independence based on mutual information," Computational Statistics & Data Analysis, Elsevier, vol. 53(7), pages 2524-2536, May.
    4. A. Dionisio & R. Menezes & D. A. Mendes, 2006. "An econophysics approach to analyse uncertainty in financial markets: an application to the Portuguese stock market," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 50(1), pages 161-164, March.
    5. Alessia Benevento & Fabrizio Durante, 2023. "Wasserstein Dissimilarity for Copula-Based Clustering of Time Series with Spatial Information," Mathematics, MDPI, vol. 12(1), pages 1-15, December.
    6. Kojadinovic, Ivan, 2010. "Hierarchical clustering of continuous variables based on the empirical copula process and permutation linkages," Computational Statistics & Data Analysis, Elsevier, vol. 54(1), pages 90-108, January.
    7. Galimberti, Giuliano & Soffritti, Gabriele, 2007. "Model-based methods to identify multiple cluster structures in a data set," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 520-536, September.
    8. Assaf, Ata & Charif, Husni & Demir, Ender, 2022. "Information sharing among cryptocurrencies: Evidence from mutual information and approximate entropy during COVID-19," Finance Research Letters, Elsevier, vol. 47(PA).
    9. Ana Belén Ramos-Guajardo, 2022. "A hierarchical clustering method for random intervals based on a similarity measure," Computational Statistics, Springer, vol. 37(1), pages 229-261, March.
    10. Gang Liu & Hui Yang, 2018. "Self-organizing network for variable clustering," Annals of Operations Research, Springer, vol. 263(1), pages 119-140, April.

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