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Identifiability of Finite Mixtures of Elliptical Distributions

Citations

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Cited by:

  1. Savi Virolainen, 2021. "Gaussian and Student's $t$ mixture vector autoregressive model with application to the effects of the Euro area monetary policy shock," Papers 2109.13648, arXiv.org, revised Jun 2024.
  2. Browne, Ryan P., 2022. "Revitalizing the multivariate elliptical leptokurtic-normal distribution and its application in model-based clustering," Statistics & Probability Letters, Elsevier, vol. 190(C).
  3. Cabral, Celso Rômulo Barbosa & Lachos, Víctor Hugo & Prates, Marcos O., 2012. "Multivariate mixture modeling using skew-normal independent distributions," Computational Statistics & Data Analysis, Elsevier, vol. 56(1), pages 126-142, January.
  4. Mika Meitz & Daniel Preve & Pentti Saikkonen, 2023. "A mixture autoregressive model based on Student’s t–distribution," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 52(2), pages 499-515, January.
  5. Battey, Heather & Linton, Oliver, 2014. "Nonparametric estimation of multivariate elliptic densities via finite mixture sieves," Journal of Multivariate Analysis, Elsevier, vol. 123(C), pages 43-67.
  6. Jason Hou-Liu & Ryan P. Browne, 2022. "Factor and hybrid components for 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. 16(2), pages 373-398, June.
  7. Galimberti, Giuliano & Soffritti, Gabriele, 2014. "A multivariate linear regression analysis using finite mixtures of t distributions," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 138-150.
  8. Naderi, Mehrdad & Hung, Wen-Liang & Lin, Tsung-I & Jamalizadeh, Ahad, 2019. "A novel mixture model using the multivariate normal mean–variance mixture of Birnbaum–Saunders distributions and its application to extrasolar planets," Journal of Multivariate Analysis, Elsevier, vol. 171(C), pages 126-138.
  9. Salvatore D. Tomarchio & Luca Bagnato & Antonio Punzo, 2022. "Model-based clustering via new parsimonious mixtures of heavy-tailed distributions," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 106(2), pages 315-347, June.
  10. Tin Lok James Ng & Andrew Zammit-Mangion, 2024. "Mixture modeling with normalizing flows for spherical density estimation," 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. 18(1), pages 103-120, March.
  11. Cristina Tortora & Brian C. Franczak & Ryan P. Browne & Paul D. McNicholas, 2019. "A Mixture of Coalesced Generalized Hyperbolic Distributions," Journal of Classification, Springer;The Classification Society, vol. 36(1), pages 26-57, April.
  12. Maruotti, Antonello & Punzo, Antonio, 2017. "Model-based time-varying clustering of multivariate longitudinal data with covariates and outliers," Computational Statistics & Data Analysis, Elsevier, vol. 113(C), pages 475-496.
  13. Nguyen, Hien D. & McLachlan, Geoffrey J., 2016. "Laplace mixture of linear experts," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 177-191.
  14. Madeleine Cule & Richard Samworth & Michael Stewart, 2010. "Maximum likelihood estimation of a multi‐dimensional log‐concave density," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(5), pages 545-607, November.
  15. Mazo, Gildas & Averyanov, Yaroslav, 2019. "Constraining kernel estimators in semiparametric copula mixture models," Computational Statistics & Data Analysis, Elsevier, vol. 138(C), pages 170-189.
  16. Jason Hou-Liu & Ryan P. Browne, 2024. "Model-Based Clustering with Nested Gaussian Clusters," Journal of Classification, Springer;The Classification Society, vol. 41(1), pages 39-64, March.
  17. Demian Pouzo & Zacharias Psaradakis & Martín Sola, 2024. "On the Robustness of Mixture Models in the Presence of Hidden Markov Regimes with Covariate-Dependent Transition Probabilities," Department of Economics Working Papers 2024_04, Universidad Torcuato Di Tella.
  18. Holzmann, Hajo & Schwaiger, Florian, 2016. "Testing for the number of states in hidden Markov models," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 318-330.
  19. Cristina Anton & Iain Smith, 2024. "Model-based clustering of functional data via mixtures of 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. 18(3), pages 563-595, September.
  20. Erik Meijer & Jelmer Ypma, 2008. "A Simple Identification Proof for a Mixture of Two Univariate Normal Distributions," Journal of Classification, Springer;The Classification Society, vol. 25(1), pages 113-123, June.
  21. Savi Virolainen, 2020. "A mixture autoregressive model based on Gaussian and Student's $t$-distributions," Papers 2003.05221, arXiv.org, revised May 2020.
  22. Fatma Zehra Doğru & Olcay Arslan, 2021. "Finite mixtures of skew Laplace normal distributions with random skewness," Computational Statistics, Springer, vol. 36(1), pages 423-447, March.
  23. Murray, Paula M. & Browne, Ryan P. & McNicholas, Paul D., 2017. "Hidden truncation hyperbolic distributions, finite mixtures thereof, and their application for clustering," Journal of Multivariate Analysis, Elsevier, vol. 161(C), pages 141-156.
  24. Qi, Xuefei & Xu, Xingbai & Feng, Zhenghui & Peng, Heng, 2025. "Component selection and variable selection for mixture regression models," Computational Statistics & Data Analysis, Elsevier, vol. 206(C).
  25. Yang, Yu-Chen & Lin, Tsung-I & Castro, Luis M. & Wang, Wan-Lun, 2020. "Extending finite mixtures of t linear mixed-effects models with concomitant covariates," Computational Statistics & Data Analysis, Elsevier, vol. 148(C).
  26. Jason Hou-Liu & Ryan P. Browne, 2022. "Chimeral Clustering," Journal of Classification, Springer;The Classification Society, vol. 39(1), pages 171-190, March.
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