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Statistical significance for hierarchical clustering

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  • Patrick K. Kimes
  • Yufeng Liu
  • David Neil Hayes
  • James Stephen Marron

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  • Patrick K. Kimes & Yufeng Liu & David Neil Hayes & James Stephen Marron, 2017. "Statistical significance for hierarchical clustering," Biometrics, The International Biometric Society, vol. 73(3), pages 811-821, September.
    • Handle: RePEc:bla:biomet:v:73:y:2017:i:3:p:811-821
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    File URL: http://hdl.handle.net/10.1111/biom.12647
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    References listed on IDEAS

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    1. Peter Hall & J. S. Marron & Amnon Neeman, 2005. "Geometric representation of high dimension, low sample size data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(3), pages 427-444, June.
    2. Borysov, Petro & Hannig, Jan & Marron, J.S., 2014. "Asymptotics of hierarchical clustering for growing dimension," Journal of Multivariate Analysis, Elsevier, vol. 124(C), pages 465-479.
    3. Liu, Yufeng & Hayes, David Neil & Nobel, Andrew & Marron, J. S, 2008. "Statistical Significance of Clustering for High-Dimension, Low–Sample Size Data," Journal of the American Statistical Association, American Statistical Association, vol. 103(483), pages 1281-1293.
    4. Baik, Jinho & Silverstein, Jack W., 2006. "Eigenvalues of large sample covariance matrices of spiked population models," Journal of Multivariate Analysis, Elsevier, vol. 97(6), pages 1382-1408, July.
    5. Nicolai Meinshausen, 2008. "Hierarchical testing of variable importance," Biometrika, Biometrika Trust, vol. 95(2), pages 265-278.
    6. Ranjan Maitra & Volodymyr Melnykov & Soumendra N. Lahiri, 2012. "Bootstrapping for Significance of Compact Clusters in Multidimensional Datasets," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(497), pages 378-392, March.
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    Cited by:

    1. Nakayama, Yugo & Yata, Kazuyoshi & Aoshima, Makoto, 2021. "Clustering by principal component analysis with Gaussian kernel in high-dimension, low-sample-size settings," Journal of Multivariate Analysis, Elsevier, vol. 185(C).
    2. Hivert, Benjamin & Agniel, Denis & Thiébaut, Rodolphe & Hejblum, Boris P., 2024. "Post-clustering difference testing: Valid inference and practical considerations with applications to ecological and biological data," Computational Statistics & Data Analysis, Elsevier, vol. 193(C).
    3. Daniel Badell & Jesica de Armas & Albert Julià, 2022. "Impact of Socioeconomic Environment on Home Social Care Service Demand and Dependent Users," IJERPH, MDPI, vol. 19(4), pages 1-21, February.
    4. Alfred Kume & Stephen G Walker, 2021. "The utility of clusters and a Hungarian clustering algorithm," PLOS ONE, Public Library of Science, vol. 16(8), pages 1-23, August.
    5. Pengcheng Zhao & Shaonian Xu & Zhenshan Huang & Pengcheng Deng & Yongming Zhang, 2021. "Identify specific gene pairs for subarachnoid hemorrhage based on wavelet analysis and genetic algorithm," PLOS ONE, Public Library of Science, vol. 16(6), pages 1-20, June.
    6. Máté Zavarkó & Attila R. Imre & Gábor Pörzse & Zoltán Csedő, 2021. "Past, Present and Near Future: An Overview of Closed, Running and Planned Biomethanation Facilities in Europe," Energies, MDPI, vol. 14(18), pages 1-27, September.
    7. Egashira, Kento & Yata, Kazuyoshi & Aoshima, Makoto, 2024. "Asymptotic properties of hierarchical clustering in high-dimensional settings," Journal of Multivariate Analysis, Elsevier, vol. 199(C).

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