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Variable Selection for Model-Based High-Dimensional Clustering and Its Application to Microarray Data

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  • Sijian Wang
  • Ji Zhu

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  • Sijian Wang & Ji Zhu, 2008. "Variable Selection for Model-Based High-Dimensional Clustering and Its Application to Microarray Data," Biometrics, The International Biometric Society, vol. 64(2), pages 440-448, June.
    • Handle: RePEc:bla:biomet:v:64:y:2008:i:2:p:440-448
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    File URL: http://hdl.handle.net/10.1111/j.1541-0420.2007.00922.x
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    References listed on IDEAS

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    1. Zou, Hui, 2006. "The Adaptive Lasso and Its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1418-1429, December.
    2. Ming Yuan & Yi Lin, 2007. "On the non‐negative garrotte estimator," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(2), pages 143-161, April.
    3. Tadesse, Mahlet G. & Sha, Naijun & Vannucci, Marina, 2005. "Bayesian Variable Selection in Clustering High-Dimensional Data," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 602-617, June.
    4. Raftery, Adrian E. & Dean, Nema, 2006. "Variable Selection for Model-Based Clustering," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 168-178, March.
    5. Fan J. & Li R., 2001. "Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1348-1360, December.
    6. Jerome H. Friedman & Jacqueline J. Meulman, 2004. "Clustering objects on subsets of attributes (with discussion)," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 66(4), pages 815-849, November.
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    Cited by:

    1. Gilles Celeux & Cathy Maugis-Rabusseau & Mohammed Sedki, 2019. "Variable selection in model-based clustering and discriminant analysis with a regularization approach," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 13(1), pages 259-278, March.
    2. Bouveyron, Charles & Brunet-Saumard, Camille, 2014. "Model-based clustering of high-dimensional data: A review," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 52-78.
    3. Mihee Lee & Haipeng Shen & Jianhua Z. Huang & J. S. Marron, 2010. "Biclustering via Sparse Singular Value Decomposition," Biometrics, The International Biometric Society, vol. 66(4), pages 1087-1095, December.
    4. Banerjee, Trambak & Mukherjee, Gourab & Radchenko, Peter, 2017. "Feature screening in large scale cluster analysis," Journal of Multivariate Analysis, Elsevier, vol. 161(C), pages 191-212.
    5. Floriello, Davide & Vitelli, Valeria, 2017. "Sparse clustering of functional data," Journal of Multivariate Analysis, Elsevier, vol. 154(C), pages 1-18.
    6. Thierry Chekouo & Alejandro Murua, 2018. "High-dimensional variable selection with the plaid mixture model for clustering," Computational Statistics, Springer, vol. 33(3), pages 1475-1496, September.
    7. Ronglai Shen & Qianxing Mo & Nikolaus Schultz & Venkatraman E Seshan & Adam B Olshen & Jason Huse & Marc Ladanyi & Chris Sander, 2012. "Integrative Subtype Discovery in Glioblastoma Using iCluster," PLOS ONE, Public Library of Science, vol. 7(4), pages 1-9, April.
    8. Brian J. Reich & Howard D. Bondell, 2011. "A Spatial Dirichlet Process Mixture Model for Clustering Population Genetics Data," Biometrics, The International Biometric Society, vol. 67(2), pages 381-390, June.
    9. Arias-Castro, Ery & Pu, Xiao, 2017. "A simple approach to sparse clustering," Computational Statistics & Data Analysis, Elsevier, vol. 105(C), pages 217-228.
    10. Isabella Morlini & Sergio Zani, 2012. "Dissimilarity and similarity measures for comparing dendrograms and their applications," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 6(2), pages 85-105, July.
    11. Peña, Daniel & Prieto Fernández, Francisco Javier & Rendon Aguirre, Janeth Carolina, 2017. "Clustering Big Data by Extreme Kurtosis Projections," DES - Working Papers. Statistics and Econometrics. WS 24522, Universidad Carlos III de Madrid. Departamento de Estadística.
    12. Pedro Galeano & Daniel Peña, 2019. "Data science, big data and statistics," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(2), pages 289-329, June.
    13. repec:jss:jstsof:47:i05 is not listed on IDEAS
    14. Baolin Wu, 2013. "Sparse cluster analysis of large-scale discrete variables with application to single nucleotide polymorphism data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 40(2), pages 358-367, February.
    15. Charles Bouveyron & Camille Brunet-Saumard, 2014. "Discriminative variable selection for clustering with the sparse Fisher-EM algorithm," Computational Statistics, Springer, vol. 29(3), pages 489-513, June.

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