IDEAS home Printed from https://ideas.repec.org/r/spr/jclass/v24y2007i2p155-181.html
   My bibliography  Save this item

Bayesian Regularization for Normal Mixture Estimation and Model-Based Clustering

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. Oscar Lao & Fan Liu & Andreas Wollstein & Manfred Kayser, 2014. "GAGA: A New Algorithm for Genomic Inference of Geographic Ancestry Reveals Fine Level Population Substructure in Europeans," PLOS Computational Biology, Public Library of Science, vol. 10(2), pages 1-11, February.
  2. Steve Su, 2016. "Flexible modelling of survival curves for censored data," Journal of Statistical Distributions and Applications, Springer, vol. 3(1), pages 1-20, December.
  3. Roberto Rocci & Stefano Antonio Gattone & Roberto Di Mari, 2018. "A data driven equivariant approach to constrained Gaussian mixture modeling," 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 235-260, June.
  4. Dolnicar, Sara & Grün, Bettina & Leisch, Friedrich, 2016. "Increasing sample size compensates for data problems in segmentation studies," Journal of Business Research, Elsevier, vol. 69(2), pages 992-999.
  5. Kim, Daeyoung & Seo, Byungtae, 2014. "Assessment of the number of components in Gaussian mixture models in the presence of multiple local maximizers," Journal of Multivariate Analysis, Elsevier, vol. 125(C), pages 100-120.
  6. Seo, Byungtae & Kim, Daeyoung, 2012. "Root selection in normal mixture models," Computational Statistics & Data Analysis, Elsevier, vol. 56(8), pages 2454-2470.
  7. Rijan Shrestha & Tomasz Kozlowski, 2016. "Inverse uncertainty quantification of input model parameters for thermal-hydraulics simulations using expectation--maximization under Bayesian framework," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(6), pages 1011-1026, May.
  8. Sucharitha, Rahul Srinivas & Lee, Seokcheon, 2022. "GMM clustering for in-depth food accessibility pattern exploration and prediction model of food demand behavior," Socio-Economic Planning Sciences, Elsevier, vol. 83(C).
  9. Branislav Panić & Jernej Klemenc & Marko Nagode, 2020. "Optimizing the Estimation of a Histogram-Bin Width—Application to the Multivariate Mixture-Model Estimation," Mathematics, MDPI, vol. 8(7), pages 1-30, July.
  10. Antonio Lentini & Huaitao Cheng & J. C. Noble & Natali Papanicolaou & Christos Coucoravas & Nathanael Andrews & Qiaolin Deng & Martin Enge & Björn Reinius, 2022. "Elastic dosage compensation by X-chromosome upregulation," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
  11. Youngdeok Hwang & Samantha Wright & Bret M. Hanlon, 2017. "Estimation and testing problems in auditory neuroscience via clustering," Biometrics, The International Biometric Society, vol. 73(3), pages 1010-1017, September.
  12. Konon, Alexander, 2016. "Career choice under uncertainty," VfS Annual Conference 2016 (Augsburg): Demographic Change 145583, Verein für Socialpolitik / German Economic Association.
  13. Sanjeena Subedi & Paul D. McNicholas, 2021. "A Variational Approximations-DIC Rubric for Parameter Estimation and Mixture Model Selection Within a Family Setting," Journal of Classification, Springer;The Classification Society, vol. 38(1), pages 89-108, April.
  14. Hsin-Hsiung Huang & Jie Yang, 2020. "Affine-transformation invariant clustering models," Journal of Statistical Distributions and Applications, Springer, vol. 7(1), pages 1-24, December.
  15. Paiva Thais & Reiter Jerome P., 2017. "Stop or Continue Data Collection: A Nonignorable Missing Data Approach for Continuous Variables," Journal of Official Statistics, Sciendo, vol. 33(3), pages 579-599, September.
  16. Roberto Mari & Roberto Rocci & Stefano Antonio Gattone, 2020. "Scale-constrained approaches for maximum likelihood estimation and model selection of clusterwise linear regression models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 29(1), pages 49-78, March.
  17. Xuekui Zhang & Gordon Robertson & Martin Krzywinski & Kaida Ning & Arnaud Droit & Steven Jones & Raphael Gottardo, 2011. "PICS: Probabilistic Inference for ChIP-seq," Biometrics, The International Biometric Society, vol. 67(1), pages 151-163, March.
  18. Derek S. Young & Xi Chen & Dilrukshi C. Hewage & Ricardo Nilo-Poyanco, 2019. "Finite mixture-of-gamma distributions: estimation, inference, and model-based clustering," 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(4), pages 1053-1082, December.
  19. Schäfer, Martin & Radon, Yvonne & Klein, Thomas & Herrmann, Sabrina & Schwender, Holger & Verveer, Peter J. & Ickstadt, Katja, 2015. "A Bayesian mixture model to quantify parameters of spatial clustering," Computational Statistics & Data Analysis, Elsevier, vol. 92(C), pages 163-176.
  20. Keefe Murphy & Thomas Brendan Murphy, 2020. "Gaussian parsimonious clustering models with covariates and a noise component," 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. 14(2), pages 293-325, June.
  21. Łuksza Marta & Kluge Bogusław & Ostrowski Jerzy & Karczmarski Jakub & Gambin Anna, 2009. "Two-Stage Model-Based Clustering for Liquid Chromatography Mass Spectrometry Data Analysis," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 8(1), pages 1-34, February.
  22. 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.
  23. Luis Angel García-Escudero & Alfonso Gordaliza & Francesca Greselin & Salvatore Ingrassia & Agustín Mayo-Iscar, 2018. "Eigenvalues and constraints in mixture modeling: geometric and computational issues," 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 203-233, June.
  24. Zhao, Jianhua & Jin, Libin & Shi, Lei, 2015. "Mixture model selection via hierarchical BIC," Computational Statistics & Data Analysis, Elsevier, vol. 88(C), pages 139-153.
  25. Oliver M Crook & Claire M Mulvey & Paul D W Kirk & Kathryn S Lilley & Laurent Gatto, 2018. "A Bayesian mixture modelling approach for spatial proteomics," PLOS Computational Biology, Public Library of Science, vol. 14(11), pages 1-29, November.
  26. Jelle R Dalenberg & Luca Nanetti & Remco J Renken & René A de Wijk & Gert J ter Horst, 2014. "Dealing with Consumer Differences in Liking during Repeated Exposure to Food; Typical Dynamics in Rating Behavior," PLOS ONE, Public Library of Science, vol. 9(3), pages 1-11, March.
  27. Csereklyei, Zsuzsanna & Thurner, Paul W. & Langer, Johannes & Küchenhoff, Helmut, 2017. "Energy paths in the European Union: A model-based clustering approach," Energy Economics, Elsevier, vol. 65(C), pages 442-457.
  28. Chi, Eric C. & Lange, Kenneth, 2014. "Stable estimation of a covariance matrix guided by nuclear norm penalties," Computational Statistics & Data Analysis, Elsevier, vol. 80(C), pages 117-128.
  29. Tin Lok James Ng & Thomas Brendan Murphy, 2021. "Model-based Clustering of Count Processes," Journal of Classification, Springer;The Classification Society, vol. 38(2), pages 188-211, July.
  30. Mathias Drton & Martyn Plummer, 2017. "A Bayesian information criterion for singular models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(2), pages 323-380, March.
  31. Thais Paiva & Jerry Reiter, 2014. "Using Imputation Techniques To Evaluate Stopping Rules In Adaptive Survey Design," Working Papers 14-40, Center for Economic Studies, U.S. Census Bureau.
  32. Daniel Fernández & Richard Arnold & Shirley Pledger & Ivy Liu & Roy Costilla, 2019. "Finite mixture biclustering of discrete type multivariate data," 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 117-143, March.
  33. Sakyajit Bhattacharya & Paul McNicholas, 2014. "A LASSO-penalized BIC for mixture model selection," 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. 8(1), pages 45-61, March.
  34. Alejandra Gonzalez-Mejia & David Styles & Paul Wilson & James Gibbons, 2018. "Metrics and methods for characterizing dairy farm intensification using farm survey data," PLOS ONE, Public Library of Science, vol. 13(5), pages 1-18, May.
  35. Pablo Cristini Guedes & Fernanda Maria Müller & Marcelo Brutti Righi, 2023. "Risk measures-based cluster methods for finance," Risk Management, Palgrave Macmillan, vol. 25(1), pages 1-56, March.
  36. 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.
  37. Patricia Gilholm & Kerrie Mengersen & Helen Thompson, 2020. "Identifying latent subgroups of children with developmental delay using Bayesian sequential updating and Dirichlet process mixture modelling," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-17, June.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.