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MCLUST: Software for Model-Based Cluster Analysis

Citations

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Cited by:

  1. Ali Pilehvar & Wedad J. Elmaghraby & Anandasivam Gopal, 2017. "Market Information and Bidder Heterogeneity in Secondary Market Online B2B Auctions," Management Science, INFORMS, vol. 63(5), pages 1493-1518, May.
  2. Adrian O’Hagan & Arthur White, 2019. "Improved model-based clustering performance using Bayesian initialization averaging," Computational Statistics, Springer, vol. 34(1), pages 201-231, March.
  3. Derek Doran & Andrew Fox, 2016. "Operationalizing Central Place and Central Flow Theory With Mobile Phone Data," Annals of Data Science, Springer, vol. 3(1), pages 1-24, March.
  4. 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.
  5. Murray, Paula M. & Browne, Ryan P. & McNicholas, Paul D., 2014. "Mixtures of skew-t factor analyzers," Computational Statistics & Data Analysis, Elsevier, vol. 77(C), pages 326-335.
  6. Ugo Fratesi & Giovanni Perucca, 2018. "Territorial capital and the resilience of European regions," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 60(2), pages 241-264, March.
  7. 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.
  8. Galimberti, Giuliano & Montanari, Angela & Viroli, Cinzia, 2009. "Penalized factor mixture analysis for variable selection in clustered data," Computational Statistics & Data Analysis, Elsevier, vol. 53(12), pages 4301-4310, October.
  9. repec:cte:wsrepe:ws1450804 is not listed on IDEAS
  10. O’Hagan, Adrian & Murphy, Thomas Brendan & Gormley, Isobel Claire, 2012. "Computational aspects of fitting mixture models via the expectation–maximization algorithm," Computational Statistics & Data Analysis, Elsevier, vol. 56(12), pages 3843-3864.
  11. De la Cruz-Mesia, Rolando & Quintana, Fernando A. & Marshall, Guillermo, 2008. "Model-based clustering for longitudinal data," Computational Statistics & Data Analysis, Elsevier, vol. 52(3), pages 1441-1457, January.
  12. Román Mínguez & José-María Montero & Gema Fernández-Avilés, 2013. "Measuring the impact of pollution on property prices in Madrid: objective versus subjective pollution indicators in spatial models," Journal of Geographical Systems, Springer, vol. 15(2), pages 169-191, April.
  13. Surajit Ray & Bruce G. Lindsay, 2008. "Model selection in high dimensions: a quadratic‐risk‐based approach," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(1), pages 95-118, February.
  14. Katherine Morris & Paul McNicholas & Luca Scrucca, 2013. "Dimension reduction for model-based clustering via mixtures of multivariate $$t$$ t -distributions," 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. 7(3), pages 321-338, September.
  15. Carlo Cavicchia & Maurizio Vichi & Giorgia Zaccaria, 2022. "Gaussian mixture model with an extended ultrametric covariance structure," 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. 16(2), pages 399-427, June.
  16. repec:jss:jstsof:18:i06 is not listed on IDEAS
  17. Paul D. McNicholas, 2016. "Model-Based Clustering," Journal of Classification, Springer;The Classification Society, vol. 33(3), pages 331-373, October.
  18. Giovanni Perucca, 2013. "Aredefinition of italian macro-areas: the role of territorial capital," RIVISTA DI ECONOMIA E STATISTICA DEL TERRITORIO, FrancoAngeli Editore, vol. 2013(2), pages 37-65.
  19. Chen, Ying-Yeh & Chen, Mengni & Lui, Carrie S.M. & Yip, Paul S.F., 2017. "Female labour force participation and suicide rates in the world," Social Science & Medicine, Elsevier, vol. 195(C), pages 61-67.
  20. Cinzia Viroli, 2010. "Dimensionally Reduced Model-Based Clustering Through Mixtures of Factor Mixture Analyzers," Journal of Classification, Springer;The Classification Society, vol. 27(3), pages 363-388, November.
  21. Geoffrey Coke & Min Tsao, 2010. "Random effects mixture models for clustering electrical load series," Journal of Time Series Analysis, Wiley Blackwell, vol. 31(6), pages 451-464, November.
  22. Andrews, Jeffrey L. & McNicholas, Paul D. & Subedi, Sanjeena, 2011. "Model-based classification via mixtures of multivariate t-distributions," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 520-529, January.
  23. Ji, Yuan & Tsui, Kam-Wah & Kim, KyungMann, 2006. "A two-stage empirical Bayes method for identifying differentially expressed genes," Computational Statistics & Data Analysis, Elsevier, vol. 50(12), pages 3592-3604, August.
  24. Shaikh Mateen R. & Beyene Joseph, 2017. "Statistical models and computational algorithms for discovering relationships in microbiome data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 16(1), pages 1-12, March.
  25. Alfonso Ibáñez & Pedro Larrañaga & Concha Bielza, 2013. "Cluster methods for assessing research performance: exploring Spanish computer science," Scientometrics, Springer;Akadémiai Kiadó, vol. 97(3), pages 571-600, December.
  26. Moustaki, Irini & Papageorgiou, Ioulia, 2005. "Latent class models for mixed variables with applications in Archaeometry," Computational Statistics & Data Analysis, Elsevier, vol. 48(3), pages 659-675, March.
  27. María Teresa Gallegos & Gunter Ritter, 2018. "Probabilistic clustering via Pareto solutions and significance tests," 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. 12(2), pages 179-202, June.
  28. Morris, Katherine & McNicholas, Paul D., 2016. "Clustering, classification, discriminant analysis, and dimension reduction via generalized hyperbolic mixtures," Computational Statistics & Data Analysis, Elsevier, vol. 97(C), pages 133-150.
  29. Mayra Z Rodriguez & Cesar H Comin & Dalcimar Casanova & Odemir M Bruno & Diego R Amancio & Luciano da F Costa & Francisco A Rodrigues, 2019. "Clustering algorithms: A comparative approach," PLOS ONE, Public Library of Science, vol. 14(1), pages 1-34, January.
  30. Morris, Katherine & McNicholas, Paul D., 2013. "Dimension reduction for model-based clustering via mixtures of shifted asymmetric Laplace distributions," Statistics & Probability Letters, Elsevier, vol. 83(9), pages 2088-2093.
  31. Katie Evans & Tanzy Love & Sally Thurston, 2015. "Outlier Identification in Model-Based Cluster Analysis," Journal of Classification, Springer;The Classification Society, vol. 32(1), pages 63-84, April.
  32. Karakos, Damianos & Khudanpur, Sanjeev & Marchette, David J. & Papamarcou, Adrian & Priebe, Carey E., 2008. "On the minimization of concave information functionals for unsupervised classification via decision trees," Statistics & Probability Letters, Elsevier, vol. 78(8), pages 975-984, June.
  33. O’Hagan, Adrian & Murphy, Thomas Brendan & Gormley, Isobel Claire & McNicholas, Paul D. & Karlis, Dimitris, 2016. "Clustering with the multivariate normal inverse Gaussian distribution," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 18-30.
  34. Torben Schubert & Andrea Bonaccorsi & Tasso Brandt & Daniela De Filippo & Benedetto Lepori & Andreas Niederl, 2014. "Is there a European university model? New evidence on national path dependence and structural convergence," Chapters, in: Andrea Bonaccorsi (ed.), Knowledge, Diversity and Performance in European Higher Education, chapter 2, pages iii-iii, Edward Elgar Publishing.
  35. Álvarez, Adolfo & Peña, Daniel, 2014. "Recombining partitions from multivariate data: a clustering method on Bayes factors," DES - Working Papers. Statistics and Econometrics. WS ws140804, Universidad Carlos III de Madrid. Departamento de Estadística.
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